{"id": "tatqa:941e08f0c9de256e74de7472019108e7", "source": "tatqa", "table_key": "26dbe7c45aeab453fcfcf8ed4407e60c045678e2", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "Year Ended", "Year Ended"], ["Stock-Based Compensation by Type of Award", "December 31, 2019", "December 31, 2018"], ["Stock options", "$2,756", "$2,926"], ["RSUs", "955", "1,129"], ["Total stock-based compensation expense", "$3,711", "$4,055"]]}, "native_split": "test", "question": "What is the total stock-based compensation expense and unrecognized stock-based compensation expense in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "10394", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Stock-based compensation expense is included in general and administrative expense for each period as follows:\nAs of December 31, 2019, there was $4,801 of unrecognized stock-based compensation expense related to unvested employee stock options and $1,882 of unrecognized stock-based compensation expense related to unvested RSUs. These costs are expected to be recognized over a weighted-average period of 2.13 and 2.33 years, respectively.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "941e08f0c9de256e74de7472019108e7", "source_table_id": "36d1468e2be20ccc765f76c712ed5e1e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:75e012945b2e63911c3bc4376b042c0d", "source": "tatqa", "table_key": "447aee3ab90b001550df59cdf0dfd542a7d07597", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 3, "section_rows": [], "cells": [["", "As of", "As of"], ["", "December 31, 2019", "December 31, 2018"], ["Deferred tax assets:", "", ""], ["Reserves and accruals", "$62", "$45"], ["Research and development credits and other credits", "1,730", "1,635"], ["Net operating loss carry forward", "27,907", "25,733"], ["Stock based compensation", "8,402", "8,857"], ["Other", "11", "26"], ["Total deferred tax assets", "38,112", "36,296"], ["Valuation allowance", "(38,112)", "(36,296)"], ["Deferred tax assets after valuation allowance", "—", "—"], ["Total deferred tax liability", "—", "—"], ["Net deferred tax assets (liabilities)", "$—", "$—"]]}, "native_split": "test", "question": "What is the percentage change in the net operating loss carry forward from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "8.45", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "In 2019 and 2018, we had pre-tax losses of $19,573 and $25,403 respectively, which are available for carry forward to offset future taxable income. We made determinations to provide full valuation allowances for our net deferred tax assets at the end of 2019 and 2018, including NOL carryforwards generated during the years, based on our evaluation of positive and negative evidence, including our history of operating losses and the uncertainty of generating future taxable income that would enable us to realize our deferred tax assets.\nDeferred tax assets (liabilities) consist of the following:\nIn assessing the realization of deferred tax assets, management considers whether it is more likely than not that a portion of the net deferred assets will not be realized. The ultimate realization of the net deferred tax assets is dependent upon the generation of future taxable income during the periods in which those temporary differences become deductible. Based on the available objective evidence, management believes it is more likely than not that the net deferred tax assets at December 31, 2019 will not be realizable. Accordingly, management has maintained a full valuation allowance against its net deferred tax assets at December 31, 2019. The net change in the total valuation allowance for the 12 months ended December 31, 2019 was an increase of $1,816.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "75e012945b2e63911c3bc4376b042c0d", "source_table_id": "8cd44786a6426d17faa6f76b0d645467"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:f5ae1658267b7a85015b2b3ae5a4b0ec", "source": "tatqa", "table_key": "fbeb19bbecf52118994a679a3aa2fcecfa3da744", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["Plan Category", "Number of Shares to be Issued Upon Exercise of Outstanding Options, Warrants and Rights", "Weighted-Average Exercise Price of Outstanding Options, Warrants and Rights (3)", "Number of shares Remaining Available for Future Issuance Under Equity Compensation Plans (Excluding Securities Reflected in 1st Column)"], ["Equity compensation plans approved by shareholders", "", "", ""], ["2001 Share Incentive Plan", "68,253(1)", "---", "---"], ["Amended and Restated 2010 Share Incentive Plan", "19,468,186(2)", "48.105", "16,684,906"], ["Amended and Restated 2010 Employee Share Purchase Plan", "---", "N/A", "30,454,275"], ["Equity compensation plans not approved by shareholders", "---", "N/A", "---"], ["Total", "19,536,441", "", "47,139,181"]]}, "native_split": "test", "question": "What is the total value of outstanding options and warrants under the Amended and Restated 2010 Share Incentive Plan? ", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "936517087.53", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "ITEM 12. SECURITY OWNERSHIP OF CERTAIN BENEFICIAL OWNERS AND MANAGEMENT AND RELATED SHAREHOLDER MATTERS\nSecurities Authorized for Issuance under Equity Compensation Plans\nThe following table sets forth, as of August 31, 2019, certain information related to our compensation plans under which Accenture plc Class A ordinary shares may be issued\n(1) Consists of 68,253 restricted share units\n(2) Consists of 19,464,437 restricted share units, with performance-based awards assuming maximum performance, and 3,751 stock options\n(3) Does not reflect restricted stock units because these awards have no exercise price.\nThe remaining information called for by Item 12 will be included in the section captioned “Beneficial Ownership” included in the definitive proxy statement relating to the 2020 Annual General Meeting of Shareholders of Accenture plc to be held on January 30, 2020 and is incorporated herein by reference. Accenture plc will file such definitive proxy statement with the SEC pursuant to Regulation 14A not later than 120 days after the end of our 2019 fiscal year covered by this Form 10-K.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 7, "source_local_id": "f5ae1658267b7a85015b2b3ae5a4b0ec", "source_table_id": "515407a98017262d3f4fcc3842da58c3"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:97bd54f9e3a19194e08d60efcd8bacc6", "source": "tatqa", "table_key": "4aecedefce420f98111ba44669fb1cd174120a90", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended June 30,", ""], ["", "2019", "2018", "2017"], ["", "", "(U.S. $ in thousands)", ""], ["Executive management", "", "", ""], ["Short-term compensation and benefits", "$3,835", "$2,991", "$2,860"], ["Post-employment benefits", "109", "99", "100"], ["Share-based payments", "17,144", "9,335", "26,030"], ["", "$21,088", "$12,425", "$28,990"], ["Board of directors", "", "", ""], ["Cash remuneration", "$430", "$362", "$388"], ["Share-based payments", "1,772", "1,577", "1,825"], ["", "$2,202", "$1,939", "$2,213"]]}, "native_split": "test", "question": "In fiscal year ended June 30, 2017, what is the percentage constitution of cash remuneration among the total compensation to the Board of directors?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "17.53", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "19. Related Party Transactions\nKey management personnel compensation\nAll directors and executive management have authority and responsibility for planning, directing and controlling the activities of the Group, and are considered to be key management personnel.\nCompensation for the Group’s key management personnel is as follows:", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "97bd54f9e3a19194e08d60efcd8bacc6", "source_table_id": "4bbe0a4d66c239a61db4574c14b7374c"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:6f4ec94a07c56cbf0ce179aa2fa32911", "source": "tatqa", "table_key": "5c3546d2a28807d880e4b7fe119112da5e65ebbf", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["(In thousands)", "Network Solutions", "Services & Support", "Total"], ["Access & Aggregation", "$289,980", "$58,894", "$348,874"], ["Subscriber Solutions & Experience (1)", "144,651", "8,269", "152,920"], ["Traditional & Other Products", "20,595", "7,672", "28,267"], ["Total", "$455,226", "$74,835", "$530,061"]]}, "native_split": "test", "question": "What percentage of the total revenue from all sources consists of total revenue from Network Solutions?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "85.88", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Note 3 – Revenue\nThe following table disaggregates our revenue by major source for the year ended December 31, 2019:\n(1) Subscriber Solutions & Experience was formerly reported as Customer Devices. With the increasing focus on enhancing the customer experience for both our business and consumer broadband customers and the addition of SmartRG during the fourth quarter of 2018, Subscriber Solutions & Experience more accurately represents this revenue category.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "6f4ec94a07c56cbf0ce179aa2fa32911", "source_table_id": "d44e4b853cdde28b616e66c5d6573cd0"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:c766af68135b6214a18b7ad5f80314e6", "source": "tatqa", "table_key": "86e7c15e982b784caa3c8800624dba28e8c06735", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["March 31,", "", ""], ["(in thousands)", "2019", "2018"], ["Raw materials", "$3,445", "$2,969"], ["Finished goods", "6,356", "6,253"], ["Total inventories", "$9,801", "$9,222"]]}, "native_split": "test", "question": "What is the percentage change in the value of raw materials in 2019 compared to 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "16.03", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Inventories and Inventory Valuation\nInventories are stated at the lower of first-in, first-out (FIFO) cost or market value. Market value is based upon an estimated average selling price reduced by estimated costs of disposal. Should actual market conditions differ from the Company’s estimates, the Company’s future results of operations could be materially affected. Reductions in inventory valuation are included in Cost of revenue in the accompanying Consolidated Statements of Operations. The Company reviews inventory for excess quantities and obsolescence based on its best estimates of future demand, product lifecycle status and product development plans. The Company uses historical information along with these future estimates to reduce the inventory cost basis. Subsequent changes in facts and circumstances do not result in the restoration or increase in that newly established cost basis. Prices anticipated for future inventory demand are compared to current and committed inventory values.\nThe components of inventories are as follows:", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "c766af68135b6214a18b7ad5f80314e6", "source_table_id": "6998ff3a821511af8a6c3d9b8c855502"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:b2f8a1335d20b34068e32e1529484c55", "source": "tatqa", "table_key": "0eb4e9fc1ddfdb7977ca48a1b01cac7b51be8585", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["", "Gross carrying value", "Weighted average estimated life (in years)"], ["Computer software", "$9.4", "5"], ["Other intangible assets:", "", ""], ["Client relationships", "19.1", "10"], ["Trade names", "1.4", "3"], ["Non-compete agreements", "0.9", "5"], ["Other intangible assets", "21.4", ""], ["Total gross carrying value", "$30.8", ""]]}, "native_split": "test", "question": "What was the difference in the gross carrying value between Trade Names and Non-compete agreements?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0.5", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Estimated Useful Lives of Computer Software and Other Intangible Assets Acquired\nAs of the acquisition date, the gross carrying value and weighted average estimated useful lives of Computer software and Other intangible assets acquired during the year ended December 31, 2019 consisted of the following (dollars in millions):", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "b2f8a1335d20b34068e32e1529484c55", "source_table_id": "56ea088b0ceec7938d4c2131cf86235d"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:a79311cf198fe3c0b698543ae20ca9b9", "source": "tatqa", "table_key": "7d13de361d53f912f1c364c5e08674b381408d41", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "Fiscal years ended July 31,", "", "", ""], ["", "2019", "2018", "Change", ""], ["", "Amount", "Amount", "($)", "(%)"], ["", "", "(In thousands, except percentages)", "", ""], ["Provision for (benefit from) income taxes", "$(8,280)", "$18,467", "(26,747)", "(145)"], ["Effective tax rate", "(66)%", "(223)%", "", ""]]}, "native_split": "test", "question": "What was the average Provision for (benefit from) income taxes for 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "5093.5", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Provision for Income Taxes\nWe are subject to taxes in the United States as well as other tax jurisdictions and countries in which we conduct business. Earnings from our non-U.S. activities are subject to local country income tax and may be subject to current U.S. income tax.\nWe recognized an income tax benefit of $8.3 million for fiscal year 2019 compared to an income tax provision of $18.5 million for fiscal year 2018. The fiscal year 2018 income tax provision was primarily due to a one-time provisional net charge from re-measuring deferred tax assets and liabilities in the quarter ended January 31, 2018 as a result of the Tax Cuts and Jobs Act (the “Tax Act”).\nThe effective tax rate of (66)% for fiscal year 2019, differs from the statutory U.S. Federal income tax rate of 21% mainly due to permanent differences for stock-based compensation, including excess tax benefits, research and development credits, the tax rate differences between the United States and foreign countries, foreign withholding taxes, and certain non-deductible expenses including executive compensation.\nAs of July 31, 2019, we had unrecognized tax benefits of $6.2 million that, if recognized, would affect our effective tax rate.\nOn December 22, 2017, the Tax Act was enacted into law which substantially changed U.S. tax law, including a reduction in the U.S. corporate income tax rate to 21% effective January 1, 2018 and several provisions that may impact us in current and future periods.\nThe Tax Act includes a provision to tax global intangible low-taxed income (“GILTI”) of foreign subsidiaries, a special deduction for foreign-derived intangible income, and a base erosion anti-abuse tax (“BEAT”) measure that taxes certain payments between a U.S. corporation and its foreign subsidiaries. These provisions of the Tax Act became effective for us beginning on August 1, 2018 and had no impact on the tax benefit for fiscal year 2019.\nUnder GAAP, we can make an accounting policy election to either treat taxes due on the GILTI inclusion as a current period expense or factor such amounts into our measurement of deferred taxes. We have elected the current period expense method. In December 2018, the Internal Revenue Service (the “IRS”) issued proposed regulations related to the BEAT tax, which we are in the process of evaluating. If the proposed BEAT regulations are finalized in their current form, the impact may be material to the tax provision in the quarter of enactment.\nThe U.S. Treasury Department, the IRS, and other standard-setting bodies will continue to interpret or issue guidance on how provisions of the Tax Act will be applied or otherwise administered. We continue to obtain, analyze, and interpret guidance as it is issued and will revise our estimates as additional information becomes available.\nAny legislative changes, including any other new or proposed U.S. Department of the Treasury regulations that have yet to be issued, may result in income tax adjustments which could be material to our provision for income taxes and effective tax rate in the period any such changes are enacted. We have finalized our assessment of the transitional impacts of the Tax Act.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 10, "source_local_id": "a79311cf198fe3c0b698543ae20ca9b9", "source_table_id": "eedaff83a4efeb4d5ea9cfcc7664b56e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:e53ab7e2d14c6360f1da8d7d34223f35", "source": "tatqa", "table_key": "726fa455cbbd2a578bc7bec7f1eb0353ffd1d74e", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["Year Ended May 31,", "", "", "", ""], ["", "", "", "Percent Change", ""], ["(Dollars in millions)", "2019", "Actual", "Constant", "2018"], ["Developed technology", "$857", "13%", "14%", "$758"], ["Cloud services and license support agreements and related relationships", "712", "-3%", "-3%", "731"], ["Other", "120", "-9%", "-9%", "131"], ["Total amortization of intangible assets", "$1,689", "4%", "4%", "$1,620"]]}, "native_split": "test", "question": "How much was the average total amortization of intangible assets in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1654.5", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Amortization of Intangible Assets: Substantially all of our intangible assets were acquired through our business combinations. We amortize our intangible assets over, and monitor the appropriateness of, the estimated useful lives of these assets. We also periodically review these intangible assets for potential impairment based upon relevant facts and circumstances. Note 6 of Notes to Consolidated Financial Statements included elsewhere in this Annual report has additional information regarding our intangible assets and related amortization.\nAmortization of intangible assets increased in fiscal 2019 compared to fiscal 2018 primarily due to additional amortization from intangible assets, which primarily included developed technology that we acquired in connection with our recent acquisitions, partially offset by a reduction in expenses associated with certain of our intangible assets that became fully amortized.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "e53ab7e2d14c6360f1da8d7d34223f35", "source_table_id": "6ba2031f27ef9758e452677e8396e806"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:c0f8990029e229b7880b128fd3d7bd12", "source": "tatqa", "table_key": "bdfe044f4f68ad20c596ee7f52a3bdbf954b0189", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31, 2018"], ["Principal amount", "$300,000", "$300,000"], ["Unamortized debt discount", "(2,691)", "(4,348)"], ["Net carrying amount before unamortized debt issuance costs", "297,309", "295,652"], ["Unamortized debt issuance costs", "(4,135)", "(6,685)"], ["Net carrying value", "$293,174", "$288,967"]]}, "native_split": "test", "question": "What is the percentage change in net carrying value between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1.46", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "9. DEBT AND OTHER FINANCING ARRANGEMENTS\n2021 Senior Convertible Notes\nIn 2017, the Company issued $300.0 million principal amount of 5.75% senior convertible notes (the “2021 Notes”) for a purchase price equal to 98% of the principal amount. The Company received net proceeds of $284.9 million, net of a discount of $6.0 million and issuance costs of $9.1 million. The debt discount is being accreted to interest expense over the term of the 2021 Notes using the interest method. The issuance costs were deferred and are being amortized to interest expense over the same term.\nThe 2021 Notes are governed by an Indenture, dated December 8, 2017 between the Company and US Bank National Association, as trustee (the “2017 Indenture”). The 2021 Notes mature on July 1, 2021, unless earlier repurchased or converted. Interest is payable semi-annually in arrears on January 1 and July 1, commencing January 1, 2018.\nThe 2021 Notes are convertible at an initial conversion rate of 23.8095 shares of the Company’s common stock per $1,000 principal amount of the 2021 Notes, which represents an initial conversion price of $42.00 per share, subject to adjustment for anti-dilutive issuances, voluntary increases in the conversion rate, and make-whole adjustments upon a fundamental change. A fundamental change includes a change in control, delisting of the Company’s common stock, and a liquidation of the Company. Upon conversion, the Company will deliver the applicable number of the Company’s common stock and cash in lieu of any fractional shares. Holders of the 2021 Notes may convert their 2021 Notes at any time prior to the close of business on the scheduled trading day immediately preceding the maturity date.\nThe holders of the 2021 Notes may require the Company to repurchase all or a portion of their 2021 Notes at a cash repurchase price equal to 100% of the principal amount of the 2021 Notes being repurchased, plus the remaining scheduled interest through and including the maturity date, upon a fundamental change and events of default, including non-payment of interest or principal and other obligations under the 2017 Indenture.\nThe net carrying amounts of the liability components of the 2021 Notes consist of the following (in thousands):\nThe effective interest rate of the liability component is 6.4% for the 2021 Notes.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 8, "source_local_id": "c0f8990029e229b7880b128fd3d7bd12", "source_table_id": "3db9dd3c463d65c10aac1bbfe899990b"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:0211f1a4a5ba753495c9b4297e014234", "source": "tatqa", "table_key": "1171bb4fb1757e6a5de7c26891b9f774d7436b96", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "As of", ""], ["", "December 31, 2019", "December 31, 2018"], ["Long-term prepaid ground rent", "$—", "$607.5"], ["Notes receivable", "1.1", "1.0"], ["Other miscellaneous assets", "405.3", "354.1"], ["Notes receivable and other non-current assets", "$406.4", "$962.6"]]}, "native_split": "test", "question": "What was long-term prepaid ground rent as a percentage of notes receivable and other non-current assets in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "63.11", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "AMERICAN TOWER CORPORATION AND SUBSIDIARIES NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (Tabular amounts in millions, unless otherwise disclosed)\n6. NOTES RECEIVABLE AND OTHER NON-CURRENT ASSETS\nNotes receivable and other non-current assets consisted of the following:\nThe reduction in Long-term prepaid ground rent is a result of the reclassification of assets to the Right-of-use asset in connection with the Company’s adoption of the new lease accounting standard.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "0211f1a4a5ba753495c9b4297e014234", "source_table_id": "c3735be8d1d086ef473b5e737ba57147"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:91add58b02eb761d380b13df7a61401a", "source": "tatqa", "table_key": "63b72a3ad354e2812fc6d90c9e1e0d50bc6461d6", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 3, "section_rows": [], "cells": [["", "June 30,", ""], ["", "2019", "2018"], ["Deferred tax assets", "", ""], ["Non-capital loss carryforwards", "$161,119", "$129,436"], ["Capital loss carryforwards", "155", "417"], ["Undeducted scientific research and development expenses", "137,253", "123,114"], ["Depreciation and amortization", "683,777", "829,369"], ["Restructuring costs and other reserves", "17,845", "17,202"], ["Deferred revenue", "53,254", "62,726"], ["Other", "59,584", "57,461"], ["Total deferred tax asset", "$1,112,987", "$1,219,725"], ["Valuation Allowance", "$(77,328)", "$(80,924)"], ["Deferred tax liabilities", "", ""], ["Scientific research and development tax credits", "$(14,482)", "$(13,342)"], ["Other", "(72,599)", "(82,668)"], ["Deferred tax liabilities", "$(87,081)", "$(96,010)"], ["Net deferred tax asset", "$948,578", "$1,042,791"], ["Comprised of:", "", ""], ["Long-term assets", "1,004,450", "1,122,729"], ["Long-term liabilities", "(55,872)", "(79,938)"], ["", "$948,578", "$1,042,791"]]}, "native_split": "test", "question": "What is the average annual total assets for both Fiscal years?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "995684.5", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "The primary components of the deferred tax assets and liabilities are as follows, for the periods indicated below:\nWe believe that sufficient uncertainty exists regarding the realization of certain deferred tax assets that a valuation allowance is required. We continue to evaluate our taxable position quarterly and consider factors by taxing jurisdiction, including but not limited to factors such as estimated taxable income, any historical experience of losses for tax purposes and the future growth of OpenText.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "91add58b02eb761d380b13df7a61401a", "source_table_id": "b3d63fb06110ad7e91c9e765227c1d27"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:4daf23d33edf0d1ba72bc66139faea81", "source": "tatqa", "table_key": "52d2665db3a2216045d965ba3346074b82c10907", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Cash and cash equivalents", "$121,398", "$148,502"], ["Availability under revolving credit facility", "261,000", "500,000"], ["Total liquidity", "$382,398", "$648,502"]]}, "native_split": "test", "question": "What was the change in total liquidity between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "266104", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Available Liquidity\nThe following table sets forth our available liquidity for the periods indicated (in thousands):\nThe decrease in total liquidity is primarily attributable to $239.0 million of outstanding revolving credit facility borrowings and $48.0 million of payments to purchase property and equipment and software and distribution rights, partially offset by positive operating cash flows.\nThe Company and Official Payments Corporation, a wholly owned subsidiary, maintain a $140.0 million uncommitted overdraft facility with Bank of America, N.A. The overdraft facility acts as a secured loan under the terms of the Credit Agreement to provide an additional funding mechanism for timing differences that can occur in the bill payment settlement process. As of December 31, 2019, $138.5 million was available.\nCash and cash equivalents consist of highly liquid investments with original maturities of three months or less. As of December 31, 2019, we had $121.4 million in cash and cash equivalents, of which $49.2 million was held by our foreign subsidiaries. If these funds were needed for our operations in the U.S., we may potentially be required to pay foreign and U.S. state income taxes to repatriate these funds. As of December 31, 2019, only the earnings in our Indian foreign subsidiaries are indefinitely reinvested. The earnings of all other foreign entities are no longer indefinitely reinvested. We are also permanently reinvested for outside book/tax basis differences related to foreign subsidiaries. These outside basis differences could reverse through sales of the foreign subsidiaries, as well as various other events, none of which are considered probable as of December 31, 2019.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "4daf23d33edf0d1ba72bc66139faea81", "source_table_id": "ed58fcb0ac048aac66d434a4391170a0"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:b031ece538ee946f2522cf3483ef4c0f", "source": "tatqa", "table_key": "49664652cf9c2e11a00b63848044b139c03c04fd", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Years Ended", ""], ["($ in millions)", "May 26, 2019", "May 27, 2018", "May 28, 2017"], ["Net derivative gains (losses) incurred", "$(3.6)", "$(0.9)", "$0.6"], ["Less: Net derivative gains (losses) allocated to reporting segments", "(1.8)", "(7.1)", "5.7"], ["Net derivative gains (losses) recognized in general corporate expenses", "$(1.8)", "$6.2", "$(5.1)"], ["Net derivative gains (losses) allocated to Grocery & Snacks", "$(2.1)", "$0.2", "$3.4"], ["Net derivative gains (losses) allocated to Refrigerated & Frozen", "(1.1)", "(0.3)", "0.8"], ["Net derivative gains (losses) allocated to International Foods .", "2.8", "(6.9)", "1.6"], ["Net derivative losses allocated to Foodservice .", "(0.6)", "(0.1)", "—"], ["Net derivative losses allocated to Pinnacle Foods", "(0.8)", "—", "—"], ["Net derivative losses allocated to Commercial .", "—", "—", "(0.1)"], ["Net derivative gains (losses) included in segment operating profit .", "$(1.8)", "$(7.1)", "$5.7"]]}, "native_split": "test", "question": "What is the ratio of net derivative gains in segment operating profit in the fiscal year 2017 to expected operating result gains in the fiscal year 2020?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "6.33", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Presentation of Derivative Gains (Losses) from Economic Hedges of Forecasted Cash Flows in Segment Results\nDerivatives used to manage commodity price risk and foreign currency risk are not designated for hedge accounting treatment. We believe these derivatives provide economic hedges of certain forecasted transactions. As such, these derivatives are generally recognized at fair market value with realized and unrealized gains and losses recognized in general corporate expenses. The gains and losses are subsequently recognized in the operating results of the reporting segments in the period in which the underlying transaction being economically hedged is included in earnings. In the event that management determines a particular derivative entered into as an economic hedge of a forecasted commodity purchase has ceased to function as an economic hedge, we cease recognizing further gains and losses on such derivatives in corporate expense and begin recognizing such gains and losses within segment operating results, immediately.\nThe following table presents the net derivative gains (losses) from economic hedges of forecasted commodity consumption and the foreign currency risk of certain forecasted transactions associated with continuing operations, under this methodology:\nAs of May 26, 2019, the cumulative amount of net derivative gains from economic hedges that had been recognized in general corporate expenses and not yet allocated to reporting segments was $1.4 million. This amount reflected net gains of $1.0 million incurred during the fiscal year ended May 26, 2019, as well as net gains of $0.4 million incurred prior to fiscal 2019. Based on our forecasts of the timing of recognition of the underlying hedged items, we expect to reclassify to segment operating results gains of $0.9 million in fiscal 2020 and $0.5 million in fiscal 2021 and thereafter.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "b031ece538ee946f2522cf3483ef4c0f", "source_table_id": "cc28d90c509efebe48d4151df5bfda73"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:82d1ed77d374cca6672422dec71a948c", "source": "tatqa", "table_key": "a83aa4e885f2751b3b31bc51d405f8bc57cb84cc", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 3, "section_rows": [], "cells": [["", "Number of Shares", "Amount"], ["", "", "(U.S. $ in thousands)"], ["Details", "", ""], ["Balance as of June 30, 2017", "91,979,704", "$9,198"], ["Conversion of Class B ordinary shares", "5,861,707", "587"], ["Exercise of share options", "1,902,084", "190"], ["Issuance for settlement of RSUs", "5,253,809", "525"], ["Vesting of share options that were early exercised", "374,496", "37"], ["Balance as of June 30, 2018", "105,371,800", "10,537"], ["Conversion of Class B ordinary shares", "5,219,947", "522"], ["Exercise of share options", "1,496,875", "150"], ["Issuance for settlement of RSUs", "4,674,873", "467"], ["Vesting of share options that were early exercised", "510,071", "51"], ["Balance as of June 30, 2019", "117,273,566", "$11,727"]]}, "native_split": "test", "question": "What is the percentage change of the total number shares between June 30, 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "11.3", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Movements in Class A ordinary share capital\nClass A shares as of June 30, 2019 and June 30, 2018 does not include 911,367 and 827,871 shares of restricted stock outstanding, respectively, that are subject to forfeiture or repurchase.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "82d1ed77d374cca6672422dec71a948c", "source_table_id": "18fcffb6cdd39b9830c130a53cbcc4f2"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:2b10bd56aa6b1c75bb53899f5f99619e", "source": "tatqa", "table_key": "ce742ff9e26c9dd6594ce06d5064db522f88836c", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year", ""], ["(In thousands, except percentages)", "2019", "2018", "2017"], ["Interest income", "$2,702", "$3,057", "$2,100"], ["Interest expense", "(53,353)", "(108,011)", "(90,288)"], ["Other Income (expense):", "", "", ""], ["Other, net", "174,734", "55,314", "(87,645)"], ["Other income (expense), net", "$124,083", "$(49,640)", "$(175,833)"], ["As a percentage of revenue", "7%", "(3)%", "(10)%"]]}, "native_split": "test", "question": "What was the change in other, net from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "119420", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Other Income (Expense), Net\nInterest expense decreased $54.7 million during fiscal 2019 as compared to fiscal 2018, primarily due to elimination of the non-recourse residential financing obligations in connection with the sale of the Residential Lease Portfolio in November 2018, as well as the elimination of the sales-leaseback financing obligations in connection with the sale of the commercial sale-leaseback portfolio during the first and second quarters of fiscal 2019.\nInterest expense increased $17.7 million in fiscal 2018 as compared to fiscal 2017 primarily due to new debt and new commercial sale-leaseback arrangements.\nOther income increased by $119.4 million during fiscal 2019 as compared to fiscal 2018, primarily due to a $158.3 million gain on an equity investment with readily determinable fair value in fiscal 2019, as compared to a loss of $6.4 million in fiscal 2018. Additionally, gain on sale of equity investments during fiscal 2019 was $17.7 million, compared to $54.2 million in fiscal 2018.\nOther income increased by $143.0 million in fiscal 2018 as compared to fiscal 2017. The change is primarily due to a $54.2 million gain on the sale of our equity method investments in fiscal 2018, a $73.0 million impairment charge in fiscal 2017 in our 8point3 Energy Partners LP equity investment balance due to the adoption of ASC 606 which materially increased the investment balance and consequently, led to the recognition of an other-than-temporary impairment in the first quarter of fiscal 2017.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "2b10bd56aa6b1c75bb53899f5f99619e", "source_table_id": "4967072c92abc844ee63806035c6bd23"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:321d0bb8c6b3d633494a6e6ad55f11e5", "source": "tatqa", "table_key": "af4923ae184f1be58f63452bba4639303386f9a6", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal", ""], ["", "2019", "2018", "2017"], ["Federal statutory tax expense", "$12,610", "$88,684", "$105,719"], ["Valuation allowance", "7,925", "4,263", "4,454"], ["Foreign taxes at rates greater (less) than U.S. rates, net", "(8,210)", "8,417", "(12,346)"], ["Stock-based compensation", "556", "(8,536)", "3,969"], ["State income taxes, net of federal income tax benefit", "1,131", "(373)", "398"], ["Research and development credit", "(3,665)", "(6,972)", "(7,884)"], ["Deferred compensation", "(206)", "(560)", "(1,022)"], ["Release of unrecognized tax benefits", "(6,688)", "(352)", "(538)"], ["Release of interest accrued for unrecognized tax benefits", "(205)", "(156)", "(78)"], ["U.S. tax reform impact", "—", "26,653", "—"], ["Deferred taxes on foreign earnings", "1,215", "—", "—"], ["Write-off of withholding tax credits", "1,134", "—", "—"], ["Other, net", "626", "3,127", "739"], ["Provision for income taxes", "$6,223", "$114,195", "$93,411"], ["Effective tax rate", "10.4%", "31.6%", "30.9%"]]}, "native_split": "test", "question": "What was the change in Valuation allowance in 2019 from 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3662", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "16. INCOME TAXES (Continued)\nThe reconciliation of the income tax expense at the U.S. Federal statutory rate (21.0% in fiscal 2019, 24.5% in fiscal 2018 and 35.0% in fiscal 2017) to actual income tax expense is as follows (in thousands):\nOn December 22, 2017, the Tax Act was enacted. The Tax Act contains significant changes to U.S. tax law, including lowering the U.S. corporate income tax rate to 21.0%, implementing a territorial tax system with a one-time transition tax assessment on previously tax-deferred foreign earnings and imposing new taxes on certain foreign-sourced income. We elected to pay the one-time transition tax over a period of up to eight years.\nIn conjunction with the Tax Act, the SEC issued guidance under Staff Accounting Bulletin No. 118 (‘‘SAB 118’’) directing taxpayers to record the impact of the Tax Act as ‘‘provisional’’ when they do not have all the necessary information to complete the accounting under ASC 740. The guidance allowed for a measurement period of up to one year after the enactment date of the Tax Act to finalize the recording of the related tax impact. In accordance with SAB 118, we recorded provisional estimates to our consolidated financial statements in fiscal 2018 based on the Tax Act. During the first quarter of fiscal 2019, we further analyzed the income tax effects of the Tax Act and determined there were no material changes to the provisional amounts disclosed in our fiscal 2018 financial statements. Although our accounting for the effects of the Tax Act is complete under SAB 118, there may be future adjustments based on interpretations by the U.S. federal and state governments and regulatory organizations, legislative updates or new regulations, or changes in accounting standards for income taxes.\nThe Tax Act also includes provisions for Global Intangible Low-Taxed Income (‘‘GILTI’’) wherein taxes on foreign income are imposed in excess of a deemed return on tangible assets of foreign corporations. In general, this income will effectively be taxed at a 10.5% tax rate reduced by any available current year foreign tax credits. This provision became effective for taxable years beginning after December 31, 2017, which was our fiscal 2019. We have elected to treat tax generated by the GILTI provisions as a period expense.\nThe effective tax rate on income from continuing operations before income taxes for fiscal 2019 of 10.4% was lower than the U.S. federal tax rate of 21.0% primarily due to the tax benefit from losses of our German subsidiaries, which are subject to higher tax rates than U.S. tax rates, adjustments related to the Tax Act’s transition tax, the net excess tax benefits from restricted stock unit vesting, the benefit of federal research and development tax credits and our Singapore and South Korea tax exemptions. These amounts are partially offset by an accrual for foreign withholding taxes on certain current year foreign earnings not considered permanently reinvested, stock-based compensation not deductible for tax purposes and limitations on the deductibility of compensation under Internal Revenue Code Section 162(m).", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "321d0bb8c6b3d633494a6e6ad55f11e5", "source_table_id": "debfaf15530ee227395897df205fa19f"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:c66f4f0f271745770edfb7f393accfd6", "source": "tatqa", "table_key": "8751b227db19b03423bb5548cae51c90f425a689", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["£m", "2019", "2018"], ["Summarised income statement", "", ""], ["Revenue", "64.0", "68.3"], ["Loss for the year", "(177.2)", "(103.7)"], ["Summarised balance sheet", "", ""], ["Investment and development property", "676.8", "841.8"], ["Borrowings – 4.125% bonds 2023", "(480.5)", "(479.5)"], ["Borrowings – compound financial instrument", "(488.5)", "(473.8)"], ["Other net liabilities", "(27.7)", "(31.2)"], ["Net liabilities", "(319.9)", "(142.7)"]]}, "native_split": "test", "question": "What is the change in revenue between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "-4.3", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "34 Subsidiaries, joint ventures and associates (continued)\nOther entities\nIntu (SGS) Finance plc and Intu Metrocentre Finance plc are consolidated as subsidiaries in these financial statements but are not listed in the table above as the Group does not own the shares in these companies. These companies are vehicles set up on behalf of the Group for the sole purpose of issuing some of the Group’s listed debt. The Group’s obligations in respect of this debt via a back-to-back intercompany loan agreement between these companies and other Group companies, and security over investment property via a deed of charge between the security trustees and other Group companies, mean that the Group is deemed to have control of these companies.\nNon-controlling interests\nBy virtue of their 40 per cent interest in The Metrocentre Partnership, GIC Real Estate is entitled to appoint 40 per cent of the directors of Metrocentre (GP) Limited. GIC Real Estate through an intermediate entity also owns a 40 per cent interest in the capital of Metrocentre Lancaster LLP. £58.2 million of the non-controlling interest losses shown in the balance sheet at 31 December 2019 (2018: £12.7 million earnings) and £70.9 million of the non-controlling interest share of loss shown in the income statement for the year ended 31 December 2019 (2018: share of loss £41.5 million) relates to GIC Real Estate’s interest in these entities. Set out below is the summarised financial information of The Metrocentre Partnership and Metrocentre LLP at 100 per cent, as consolidated:\nThe balance sheet includes a non-controlling interest recoverable amount of £58.2 million (2018: £12.7 million attributable to noncontrolling interest). This amount is considered to be recoverable in view of the £195.4 million owed to the non-controlling interest (which is included in the Group’s borrowings in note 23).", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "c66f4f0f271745770edfb7f393accfd6", "source_table_id": "1614d46700363e699110c89aaf7e5ba5"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:5e354ce404fe55213d485be6172f32c0", "source": "tatqa", "table_key": "cd499bf4d09de1d5ffc52e26075b89120397aac3", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 3, "section_rows": [], "cells": [["$ million", "2019", "2018"], ["Cash flow from operations", "124.9", "65.9"], ["Tax paid", "(5.6)", "(5.7)"], ["Net cash inflow from operating activities", "119.3", "60.2"], ["Interest received", "2.6", "1.3"], ["Net capital expenditure", "(11.9)", "(10.6)"], ["Payment of lease liabilities, principal and interest1", "(10.3)", "—"], ["Lease payments received from finance leases", "0.4", "—"], ["Free cash flow", "100.1", "50.9"]]}, "native_split": "test", "question": "What was the change in free cash flow?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "49.2", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Financing and cash flow\nThe Group delivered very strong cash generation in 2019, driven by higher operating profit and effective working capital management. Changes in working capital, reflected within cash flow from operations, benefited from:\n• our continued focus on improving trade receivables collection; • a reduction in inventory levels due to a high level of shipments at the end of 2019; and • growth in payables, resulting from the increase in activity levels and emphasis on extending supplier payment terms.\nFree cash flow for 2019 almost doubled year-on-year coming in at $100.1 million, compared to $50.9 million in 2018, resulting in a free cash flow conversion which represented 123 per cent of adjusted earnings (2018 77 per cent).\nFree cash flow is set out below:\nNote\n1. Spirent adopted IFRS 16 on 1 January 2019; in prior periods operating lease payments were included within cash flow from operations.\nFree cash flow includes a net cash outflow in respect of exceptional items in 2018 and 2019 of $5.5 million (2018 $3.6 million in respect of exceptional items charged in 2017 and 2018).\nTax payments of $5.6 million made in 2019 were consistent with the prior year (2018 $5.7 million). Net capital expenditure of $11.9 million was also broadly consistent with the prior year (2018 $10.6 million), with the incremental spend of $1.3 million primarily related to investment in 5G. We continue to exercise careful management of capital investment to ensure efficient use of capital and maximise return on investment.\nFollowing the adoption of IFRS 16 on 1 January 2019, the payment of lease liabilities, both the principal and interest elements, are shown separately from net cash flow from operating activities. In previous periods they would have been reflected in that number. There is no overall impact in comparing free cash flow year-on-year.\nIn 2019, the final dividend for 2018 and an interim dividend for 2019 totalling $28.6 million were paid. This compared to total dividends of $54.8 million paid in 2018, including a special dividend of $29.9 million. In addition, 4.0 million shares were purchased and placed into the Employee Share Ownership Trust at a cost of $8.6 million (2018 1.5 million shares at a net cost of $2.5 million) and $1.9 million of cash consideration was paid to acquire the business of a navigation systems company based in the United Kingdom.\nFollowing these payments, cash and cash equivalents closed at $183.2 million at 31 December 2019, compared with $121.6 million at 31 December 2018. There continues to be no bank debt.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 12, "source_local_id": "5e354ce404fe55213d485be6172f32c0", "source_table_id": "902a9a9cf29ffa7b1434e0e604e0c8ea"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:adc3b71f6883dd42f4a6ddbf93f95da0", "source": "tatqa", "table_key": "8bd91181997cd6e1314f275a89c08bda99457e0c", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 7, "section_rows": [], "cells": [["", "Carrying Value (Denominated Currency) (1)", "", "Carrying Value (USD) (1)", "", "Interest Rate", "Maturity Date"], ["", "2019", "2018", "2019", "2018", "", ""], ["South African Credit Facility (2)", "288.7", "577.4", "$20.6", "$40.2", "8.75%", "December 17, 2020"], ["Colombian Credit Facility (3)", "79,647.3", "109,193.8", "$24.3", "$33.6", "8.13%", "April 24, 2021"], ["Brazil Credit Facility (4)", "65.4", "94.7", "$16.2", "$24.4", "Various", "January 15, 2022"], ["Kenya Debt (5)", "29.6", "51.8", "$29.6", "$51.8", "8.00%", "October 1, 2021"], ["U.S. Subsidiary Debt (6)", "1.9", "2.5", "$1.9", "$2.5", "—%", "January 1, 2022"], ["Eaton Towers Debt (7):", "", "", "", "", "", ""], ["USD Denominated (8)", "238.8", "—", "$238.8", "$—", "Various", "Various"], ["EUR Denominated", "26.2", "—", "$29.5", "$—", "Various", "Various"], ["XOF Denominated", "16,836.8", "—", "$28.8", "$—", "Various", "Various"], ["KES Denominated (8)", "3,319.2", "—", "$ 32.7", "$—", "Various", "Various"]]}, "native_split": "test", "question": "What was the sum of the three highest carrying value (USD) in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "125.6", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Other Subsidiary Debt— The Company’s other subsidiary debt includes (i) a credit facility entered into by one of the Company’s South African subsidiaries in December 2015, as amended (the “South African Credit Facility”), (ii) a long-term credit facility entered into by one of the Company’s Colombian subsidiaries in October 2014 (the “Colombian Credit Facility”), (iii) a credit facility entered into by one of the Company’s Brazilian subsidiaries in December 2014 (the “Brazil Credit Facility”) with Banco Nacional de Desenvolvimento Econômico e Social, (iv) a note entered into by one of the Company’s subsidiaries in October 2018 in connection with the acquisition of sites in Kenya (the “Kenya Debt”), (v) U.S. subsidiary debt related to a seller-financed acquisition (the “U.S. Subsidiary Debt”) and (vi) debt entered into by certain Eaton Towers subsidiaries acquired in connection with the Eaton Towers Acquisition (the “Eaton Towers Debt”).\nAmounts outstanding and key terms of other subsidiary debt consisted of the following as of December 31, (in millions, except percentages):\n(1) Includes applicable deferred financing costs.\n(2) Denominated in ZAR, with an original principal amount of 830.0 million ZAR. On December 23, 2016, the borrower borrowed an additional 500.0 million ZAR. Debt accrues interest at a variable rate. The borrower no longer maintains the ability to draw on the South African Credit Facility.\n(3) Denominated in COP, with an original principal amount of 200.0 billion COP. Debt accrues interest at a variable rate. The loan agreement for the Colombian Credit Facility requires that the borrower manage exposure to variability in interest rates on certain of the amounts outstanding under the Colombian Credit Facility. The borrower no longer maintains the ability to draw on the Colombian Credit Facility.\n(4) Denominated in BRL, with an original principal amount of 271.0 million BRL. Debt accrues interest at a variable rate. The borrower no longer maintains the ability to draw on the Brazil Credit Facility.\n(5) Denominated in USD, with an original principal amount of $51.8 million. The loan agreement for the Kenya Debt requires that the debt be paid either (i) in future installments subject to the satisfaction of specified conditions or (ii) three years from the note origination date.\n(6) Related to a seller-financed acquisition. Denominated in USD with an original principal amount of $2.5 million.\n(7) Related to the Eaton Towers Acquisition. Denominated in multiple currencies, including USD, EUR, KES and XOF. Amounts shown represent principal outstanding as of December 31, 2019.\n(8) Subsequent to December 31, 2019, the Company repaid all of the outstanding USD denominated and KES denominated debt.\nPursuant to the agreements governing the South African Credit Facility and the Colombian Credit Facility, payments of principal and interest are generally payable quarterly in arrears. Outstanding principal and accrued but unpaid interest will be due and payable in full at maturity. The South African Credit Facility may be prepaid in whole or in part without prepayment consideration. The Colombian Credit Facility may be prepaid in whole or in part at any time, subject to certain limitations and prepayment consideration.\nThe South African Credit Facility, the Colombian Credit Facility and the Brazil Credit Facility are secured by, among other things, liens on towers owned by the applicable borrower.\nEach of the agreements governing the other subsidiary debt contains contractual covenants and other restrictions. Failure to comply with certain of the financial and operating covenants could constitute a default under the applicable debt agreement, which could result in, among other things, the amounts outstanding, including all accrued interest and unpaid fees, becoming immediately due and payable.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 13, "source_local_id": "adc3b71f6883dd42f4a6ddbf93f95da0", "source_table_id": "8904a4eb6ba20505519dbbd57dd120bf"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:040780038bc919431b003eb107daf8f8", "source": "tatqa", "table_key": "c33dc201020005e5fbca2dfd3bba9d77652168d3", "grid": {"TD": 1, "LD": 0, "nrows": 18, "ncols": 4, "section_rows": [], "cells": [["", "Estimated at Acquisition and as of December 31, 2018", "Adjustments", "Final as of December 31, 2019"], ["1,666,667 shares of common stock, at fair value of $3.00 per share at closing", "$                          5,000", "$                        -", "$                      5,000"], ["Golden Ridge financial liabilities paid for the seller", "2,661", "-", "2,661"], ["Cash", "250", "-", "250"], ["Note payable to seller", "609", "-", "609"], ["Working capital adjustment to purchase price", "(1,147)", "584", "(563)"], ["Total fair value of consideration transferred", "7,373", "584", "7,957"], ["Cash", "409", "(63)", "346"], ["Accounts receivable", "1,587", "87", "1,674"], ["Inventories", "103", "-", "103"], ["Property and equipment", "5,092", "-", "5,092"], ["Accounts payable", "(222)", "110", "(112)"], ["Commodities payable", "(2,559)", "432", "(2,127)"], ["Accrued liabilities", "(12)", "12", "-"], ["Lease liabilities", "(104)", "-", "(104)"], ["Equipment notes payable", "(99)", "6", "(93)"], ["Net recognized amounts of identifiable assets acquired and liabilities assumed", "4,195", "584", "4,779"], ["Goodwill", "$ 3,178", "$                      -", "$ 3,178"]]}, "native_split": "test", "question": "What is the percentage change in goodwill between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Golden Ridge\nIn November 2018, we acquired substantially all of the assets comprising the business of Golden Ridge Rice Mills, LLC, now conducting business as Golden Ridge Rice Mills, Inc. (Golden Ridge). The primary activity of the business is the operation of a rice mill in Wynne, Arkansas. We acquired the business as part of our strategy to vertically integrate in order to leverage our proprietary technologies for producing SRB and derivative products. The acquisition has been accounted for as a business combination. The results of Golden Ridge’s operations are included in our consolidated financial statements beginning November 28, 2018. In 2018, we incurred $0.1 million of Golden Ridge acquisition-related costs which are included in selling, general and administrative expenses.\nThe purchase price for Golden Ridge was subject to adjustment if the estimated working capital with respect to the assets purchased and the liabilities assumed at the time of closing was different than the actual closing working capital, as defined in the purchase agreement. We revised our preliminary estimate of the working capital adjustment as indicated in the table below. The following table summarizes the purchase price allocation as of closing and as revised (in thousands, except share and per share amounts).\nThe 1,666,667 shares issued at closing of our purchase of Golden Ridge included 380,952 shares that were deposited in an escrow account to be used to satisfy any indemnification obligations of the seller that may arise. As of December 31, 2018, the 380,952 shares remained in escrow. In July 2019, we reached an agreement to settle the $0.6 million working capital adjustment receivable and other claims with the sellers of Golden Ridge. As a result, (i) 340,000 shares of common stock held in the escrow account ($1.0 million fair value as of both the settlement date and the November 28, 2018, acquisition date) were returned to us and retired, (ii) the remaining $0.4 million note payable we owed to a seller was cancelled and (iii) certain open grain purchase contracts with entities related to a seller were terminated. We recorded a gain on the noncash settlement of $0.8 million in the third quarter of 2019, which is included in other income. In connection with the foregoing, a settlement agreement was entered into among the parties. All shares of common stock were distributed and the escrow agreement was terminated.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "040780038bc919431b003eb107daf8f8", "source_table_id": "1bcc157b8f0fb5225f4f574489e4c8a1"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:844c4d74af9231f62c7c193d54c0549e", "source": "tatqa", "table_key": "306acb8da2986075e4460e30433ab50d2abeff43", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 6, "section_rows": [], "cells": [["", "Years ended December 31,", "", "", "2019 vs 2018", "2018 vs 2017"], ["", "2019", "2018", "2017", "% Change", "% Change"], ["", "(in thousands, except percentages)", "", "", "", ""], ["Sales and marketing", "$ 472,841", "$ 350,069", "$ 225,694", "35.1 %", "55.1 %"], ["Percentage of total revenues", "30.0 %", "32.6 %", "33.5 %", "", ""]]}, "native_split": "test", "question": "What is the average sales and marketing expenses for 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "411455", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Operating Expense\nSales and Marketing\nSales and marketing expenses increased $122.8 million, or 35.1%, for the year ended December 31, 2019 compared to the same period in 2018, due to an increase of $70.4 million in expenditures on marketing programs to support the growth of our business, such as advertisements on search engines and social media, brand campaigns, event sponsorships and payments to partners. Employee-related costs increased $48.7 million ($14.1 million of which related to stock-based compensation and related payroll taxes) to support the growth of the business including in Shopify Plus and International operations. Computer hardware and software costs increased by $3.7 million, largely due to the growth in sales and marketing headcount\nSales and marketing expenses increased $124.4 million, or 55.1%, for the year ended December 31, 2018 compared to the same period in 2017, primarily due to an increase of $80.7 million in employee-related costs. In addition to employee-related costs, marketing costs increased by $39.7 million and computer hardware and software costs increased by $4.0 million.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "844c4d74af9231f62c7c193d54c0549e", "source_table_id": "6ad2ec5c0b711b3fb827cb77b1ebecbe"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:cf42ab5eb0e02d2fa26ecbd6f525fb13", "source": "tatqa", "table_key": "92b21e2a6aaa43a35bcc0fbdff7138ed113f1bbf", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Land and land improvements", "$1,095", "$1,136"], ["Buildings and improvements", "68,350", "70,522"], ["Machinery and equipment", "224,312", "231,619"], ["Less: Accumulated depreciation", "(188,719)", "(203,876)"], ["Property, plant and equipment, net", "$105,038", "$99,401"]]}, "native_split": "test", "question": "What was the percentage change in the the total net property, plant and equipment between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "5.67", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nNOTE 5 — Property, Plant and Equipment\nProperty, plant and equipment is comprised of the following:", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "cf42ab5eb0e02d2fa26ecbd6f525fb13", "source_table_id": "903322d78b47417847ca16d46feb6653"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:0d4eeda9b398c1aa8fd598019e3867c4", "source": "tatqa", "table_key": "21ce07679a659e052efaf87a77b39c066e1f4ef4", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 3, "section_rows": [], "cells": [["", "June 30,", ""], ["Core", "2019", "2018"], ["Beginning balance", "$195,956", "$195,956"], ["Goodwill, acquired during the year", "4,000", "—"], ["Goodwill, adjustments related to dispositions", "—", "—"], ["Ending balance", "$199,956", "$195,956"], ["Payments", "", ""], ["Beginning balance", "$325,204", "$234,106"], ["Goodwill, acquired during the year", "122", "91,098"], ["Goodwill, adjustments related to dispositions", "—", "—"], ["Ending balance", "$325,326", "$325,204"], ["Complementary", "", ""], ["Beginning balance", "$128,769", "$122,403"], ["Goodwill, acquired during the year", "12,893", "6,499"], ["Goodwill, adjustments related to dispositions", "—", "(133)"], ["Ending balance", "$141,662", "$128,769"]]}, "native_split": "test", "question": "What is the average ending carrying amount of goodwill under the payment segment for 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "325265", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "NOTE 4. OTHER ASSETS\nGoodwill\nThe carrying amount of goodwill for the fiscal years ended June 30, 2019 and 2018, by reportable segments, is as follows:\nGoodwill acquired during fiscal 2019 totaled $17,015, with $12,893 of that resulting from the purchase of BOLTS Technologies, Inc., $3,999 resulting from the purchase of Agiletics, Inc., and the remainder resulting from a measurement period adjustment on the Ensenta valuation. The goodwill arising from these acquisitions consists largely of the growth potential, synergies and economies of scale expected from combining the operations of the Company with those of BOLTS Technologies and Agiletics, together with the value of their assembled workforces. No goodwill was assigned to the Company’s Corporate and Other reportable segment.\nGoodwill acquired during fiscal 2018 totaled $97,597, with $91,098 of that resulting from the purchase of Ensenta Corporation, included in the Payments segment. The remaining $6,499 of goodwill acquired during fiscal 2018 resulted from the purchase of Vanguard Software Group, which was added to the Company’s Complementary segment. The goodwill arising from these acquisitions consists largely of the growth potential, synergies and economies of scale expected from combining the operations of the Company with those of Ensenta and Vanguard, together with the value of their assembled workforces. No goodwill was assigned to the Company’s Corporate and Other reportable segment.\nThe Goodwill reduction during fiscal 2018 was a result of the Company’s sale of jhaDirect product line in the first quarter. Goodwill allocated to the carrying amount of the net assets sold was calculated based on the relative fair values of the business disposed and the portion of the reporting unit that was retained.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "0d4eeda9b398c1aa8fd598019e3867c4", "source_table_id": "f24fa8c4247731d58c943302291b95c4"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:e1630c53af12657dfb9a2ebff91e3641", "source": "tatqa", "table_key": "e46d82e19d87d10d71ba847105d54c41f7744d03", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "Target Allocations", "Percentage of Plan Assets at December 31,", ""], ["Asset Category", "2020", "2019", "2018"], ["Equity securities", "13%", "13%", "12%"], ["Debt securities", "83%", "83%", "84%"], ["Other", "4%", "4%", "4%"], ["Total", "100%", "100%", "100%"]]}, "native_split": "test", "question": "What was the difference between the target allocations of Debt Securities and Other assets in 2020?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "79", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nOur pension plan asset allocation at December 31, 2019, and 2018, and target allocation for 2020 by asset category are as follows:\nWe employ a liability-driven investment strategy whereby a mix of equity and fixed-income investments are used to pursue a derisking strategy which over time seeks to reduce interest rate mismatch risk and other risks while achieving a return that matches or exceeds the growth in projected pension plan liabilities. Risk tolerance is established through careful consideration of plan liabilities and funded status. The investment portfolio primarily contains a diversified mix of equity and fixed-income investments. Other assets such as private equity are used modestly to enhance long-term returns while improving portfolio diversification. Investment risk is measured and monitored on an ongoing basis through quarterly investment portfolio reviews, annual liability measurements, and asset/liability studies at regular intervals.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "e1630c53af12657dfb9a2ebff91e3641", "source_table_id": "054153aec5a8b7066b1083f3ec3515ed"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:5fbdfcb6a47b1714e3887cbfbd07bbb3", "source": "tatqa", "table_key": "de5663f638985f47ca915e6cd178b2add483893e", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["Fiscal Year Ended", "", "", ""], ["", "December 27, 2019", "December 28, 2018", "December 29, 2017"], ["Restricted Share Awards (“RSAs”)", "132,861", "42", "84,511"], ["Stock options", "—", "—", "201,799"], ["Convertible notes", "76,384", "—", "—"]]}, "native_split": "test", "question": "What is the change in the Restricted Share Awards (“RSAs”) between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "132819", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Note 3 – Net Income per Share\nPotentially dilutive securities that have been excluded from the calculation of diluted net income per common share because the effect is anti-dilutive are as follows:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "5fbdfcb6a47b1714e3887cbfbd07bbb3", "source_table_id": "057149f07d2fad98ce7387e82475f309"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:ea584a4934a90372dbba4c4951a86875", "source": "tatqa", "table_key": "4402e2c2f4023360356fe24b23da5b90f2ebb60a", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Subscription revenue", "$542,968", "$473,052", "$396,764"], ["Percentage of subscription revenue to total revenue", "94.2%", "87.9%", "82.3%"], ["Professional services revenue", "$33,555", "$64,839", "$85,221"], ["Percentage of professional services to total revenue", "5.8%", "12.1%", "17.7%"], ["Total revenue", "$576,523", "537,891", "481,985"]]}, "native_split": "test", "question": "What was the change in Professional services revenue between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "-31284", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "The following table sets forth our sources of revenue for each of the periods indicated (in thousands, except for percentages):\nSubscription revenue increased by $69.9 million, or 15%, in 2019 when compared to 2018. Subscription revenue growth on a constant currency basis increased 16% in 2019 when compared to 2018. The increase was attributable to new business, which includes new customers, upsells, cross-sells, and renewals from existing customers.\nProfessional services revenue decreased by $31.3 million, or 48%, in 2019 when compared to 2018. The decrease of professional services revenue is attributable to the continued migration of implementation services to our global partners.\nSubscription revenue increased by $76.3 million, or 19%, in 2018 when compared to 2017. The increase was attributable to new business, which included new customers, upsells, and renewals from existing customers. Professional services revenue decreased by $20.4 million, or 24%, in 2018 when compared to 2017. The decrease of professional services revenue is attributable to the execution of our strategic initiative to migrate much of our implementation services to our global partners.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "ea584a4934a90372dbba4c4951a86875", "source_table_id": "596a9a79bc3d68a5fedca7987319a5f9"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:0d6caf7adf008d5425db9d32fe3d5f86", "source": "tatqa", "table_key": "9b810f5c7f47e54909b43fd76b929d2c0a6ae03c", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["", "2019", "2018", "2017"], ["United States", "-16,317", "$16,312", "-42,863"], ["Foreign", "88,527", "75,487", "86,435"], ["Total", "$72,210", "$91,799", "$43,572"]]}, "native_split": "test", "question": "What is the percentage change in income from Foreign countries between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "17.27", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "13. Income Taxes\nOn December 22, 2017, the Tax Cuts and Jobs Act (“Tax Act”) was signed into U.S. Law. As of December 31, 2018, the Company had completed its accounting for the tax effects related to the enactment of the Tax Act.\nThe Tax Act reduced the U.S. federal corporate income tax rate from 35% to 21%, effective January 1, 2018. During the year ended December 31, 2017, the Company remeasured certain deferred tax assets and liabilities and recorded a $15.0 million provisional tax charge. During the year ended December 31, 2018, the Company reduced the initial provisional tax charge by recording a $4.9 million benefit related to accelerated tax deductions claimed on the 2018 U.S. Federal Income Tax Return.\nThe Tax Act required U.S. companies to pay a one-time transition tax on certain unremitted foreign earnings. During the year ended December 31, 2017, the Company recorded a $20.9 million provisional tax charge based on post-1986 earnings and profits of foreign subsidiaries that were previously deferred from U.S. income taxes. Upon further analysis, the Company reduced the initial provisional tax charge by recording an $8.1 million benefit during the year ended December 31, 2018.\nDuring the year ended December 31, 2018, the Company recorded a $15.5 million valuation allowance on its deferred tax asset related to U.S. foreign tax credits based upon business conditions and tax laws in effect at that time.\nDuring the year ended December 31, 2019, following the acquisition of Speedpay, the Company determined it will more likely than not be able to utilize foreign tax credits in future years due to additional income generated by Speedpay; therefore, the Company released the $15.5 million valuation allowance that had been established on this deferred tax asset.\nThe Tax Act subjects a U.S. shareholder to tax on global intangible low-taxed income (\"GILTI\") earned by certain foreign subsidiaries. The Company has elected to account for GILTI in the year the tax is incurred.\nPrior to 2018, the Company considered all earnings in foreign subsidiaries to be indefinitely reinvested, and accordingly, recorded no deferred income taxes related to unremitted earnings. As of December 31, 2019 and 2018, the Company considered only the earnings in its Indian subsidiaries to be indefinitely reinvested. The earnings of all other foreign subsidiaries are no longer considered indefinitely reinvested. The Company is also permanently reinvested for outside book/tax basis differences related to foreign subsidiaries.\nFor financial reporting purposes, income before income taxes includes the following components (in thousands):", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 9, "source_local_id": "0d6caf7adf008d5425db9d32fe3d5f86", "source_table_id": "4ab898d6877c171a1e67f9be919c5e68"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:3111bd0fd3138287e5e124b10d7f9b18", "source": "tatqa", "table_key": "265b498efd58cb66135bf2c21ac0f24bf7d446e7", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["", "2019", "2018", "2017"], ["", "", "(in thousands, except per share amounts)", ""], ["Numerator:", "", "", ""], ["Net loss", "$(19,898)", "$(26,199)", "$(9,187)"], ["Denominator:", "", "", ""], ["Weighted average common shares outstanding—basic", "71,005", "68,490", "66,252"], ["Dilutive common stock equivalents", "—", "—", "—"], ["Weighted average common shares outstanding—diluted", "71,005", "68,490", "66,252"], ["Net loss per share:", "", "", ""], ["Basic", "$(0.28)", "$(0.38)", "$(0.14)"], ["Diluted", "$(0.28)", "$(0.38)", "$(0.14)"]]}, "native_split": "test", "question": "What is the average Numerator: Net loss for the year ended December 31, 2019 to 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "23048.5", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "2. Net Income (Loss) Per Share\nBasic earnings per share, or EPS, is calculated by dividing net income (loss) by the weighted-average number of common shares outstanding for the period, without consideration for common stock equivalents. Diluted EPS is computed by dividing net income by the weighted-average number of common shares outstanding for the period and the weighted-average number of dilutive common stock equivalents outstanding for the period determined using the treasury-stock method.\nFor purposes of this calculation, common stock options, restricted stock units and restricted stock awards are considered to be common stock equivalents and are only included in the calculation of diluted EPS when their effect is dilutive. In periods in which the Company has a net loss, dilutive common stock equivalents are excluded from the calculation of diluted EPS.\nThe table below presents the computation of basic and diluted earnings per share:\nFor the years ended December 31, 2019, 2018 and 2017, the Company incurred net losses and accordingly excluded common stock equivalents for outstanding stockbased awards, which represented all potentially dilutive securities, of 2.5 million, 3.7 million, and 4.5 million, respectively, from the calculation of diluted net loss per share due to their anti-dilutive nature.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "3111bd0fd3138287e5e124b10d7f9b18", "source_table_id": "4bf6297fe4aaf51d58b160a778fadc6f"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:164087aa6f7682c089f0c2afcec69549", "source": "tatqa", "table_key": "fbeb19bbecf52118994a679a3aa2fcecfa3da744", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["Plan Category", "Number of Shares to be Issued Upon Exercise of Outstanding Options, Warrants and Rights", "Weighted-Average Exercise Price of Outstanding Options, Warrants and Rights (3)", "Number of shares Remaining Available for Future Issuance Under Equity Compensation Plans (Excluding Securities Reflected in 1st Column)"], ["Equity compensation plans approved by shareholders", "", "", ""], ["2001 Share Incentive Plan", "68,253(1)", "---", "---"], ["Amended and Restated 2010 Share Incentive Plan", "19,468,186(2)", "48.105", "16,684,906"], ["Amended and Restated 2010 Employee Share Purchase Plan", "---", "N/A", "30,454,275"], ["Equity compensation plans not approved by shareholders", "---", "N/A", "---"], ["Total", "19,536,441", "", "47,139,181"]]}, "native_split": "test", "question": "What proportion of the Number of Shares to be Issued Upon Exercise of Outstanding Options, Warrants and Rights are shares from the 2001 share incentive plan?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0.35", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "ITEM 12. SECURITY OWNERSHIP OF CERTAIN BENEFICIAL OWNERS AND MANAGEMENT AND RELATED SHAREHOLDER MATTERS\nSecurities Authorized for Issuance under Equity Compensation Plans\nThe following table sets forth, as of August 31, 2019, certain information related to our compensation plans under which Accenture plc Class A ordinary shares may be issued\n(1) Consists of 68,253 restricted share units\n(2) Consists of 19,464,437 restricted share units, with performance-based awards assuming maximum performance, and 3,751 stock options\n(3) Does not reflect restricted stock units because these awards have no exercise price.\nThe remaining information called for by Item 12 will be included in the section captioned “Beneficial Ownership” included in the definitive proxy statement relating to the 2020 Annual General Meeting of Shareholders of Accenture plc to be held on January 30, 2020 and is incorporated herein by reference. Accenture plc will file such definitive proxy statement with the SEC pursuant to Regulation 14A not later than 120 days after the end of our 2019 fiscal year covered by this Form 10-K.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 7, "source_local_id": "164087aa6f7682c089f0c2afcec69549", "source_table_id": "515407a98017262d3f4fcc3842da58c3"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:2b2b0bc8e3053297b02377d9c18e0df7", "source": "tatqa", "table_key": "437bb9d3f25eba682d21f1393411adee32e04fc3", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "Number of Shares", "Weighted-Average Grant\nDate Fair Value"], ["Nonvested shares of RSUs at December 31, 2018", "8.6", "$16.59"], ["Granted", "5.4", "21.64"], ["Achieved", "0.2", "24.46"], ["Released", "(4.8)", "14.41"], ["Canceled", "(0.5)", "19.74"], ["Nonvested shares of RSUs at December 31, 2019", "8.9", "20.84"]]}, "native_split": "test", "question": "What is the change in the Number of Nonvested shares of RSUs from December 31, 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0.3", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "Restricted Stock Units\nA summary of the RSU transactions for the year ended December 31, 2019 are as follows (number of shares in millions):\nDuring 2019, the Company awarded 2.6 million RSUs to certain officers and employees of the Company that vest upon the achievement of certain performance criteria and market conditions. The number of units expected to vest is evaluated each reporting period and compensation expense is recognized for those units for which achievement of the performance criteria is considered probable. Compensation expense for RSUs with market conditions are recognized based on the grant date fair value irrespective of the achievement of the condition.\nAs of December 31, 2019, unrecognized compensation expense, net of estimated forfeitures related to non-vested RSUs granted under the Amended and Restated SIP with service, performance and market conditions, was $60.9 million, $10.1 million and $3.9 million, respectively.\nFor RSUs with time-based service conditions, expense is being recognized over the vesting period; for RSUs with performance criteria, expense is recognized over the period during which the performance criteria is expected to be achieved; for RSUs with market conditions expense is recognized over the period in which the condition is assessed irrespective of whether it would be achieved or not.\nUnrecognized compensation cost related to awards with certain performance criteria that are not expected to be achieved is not included here. Total compensation expense related to performance-based, service-based, and market-based RSUs was $69.8 million for the year ended December 31, 2019, which included $48.4 million for RSUs with time-based service conditions that were granted in 2019 and prior that are expected to vest.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "2b2b0bc8e3053297b02377d9c18e0df7", "source_table_id": "8bf3df18a086f04608ee6da57046af30"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:5ce893d110be3ff0b2ef7e9794c22cbc", "source": "tatqa", "table_key": "3a8102236105b4c2be20bb7dadd2d6f7710c96a2", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["(In millions)", "", "", ""], ["Year Ended June 30,", "2019", "2018", "2017"], ["Balance, beginning of period", "$   397", "$  361", "$  409"], ["Charged to costs and other", "153", "134", "58"], ["Write-offs", "(116)", "(98)", "(106)"], ["Balance, end of period", "$ 434", "$  397", "$  361"]]}, "native_split": "test", "question": "What was the change in the balance for 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "37", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Contract Balances\nTiming of revenue recognition may differ from the timing of invoicing to customers. We record a receivable when revenue is recognized prior to invoicing, or unearned revenue when revenue is recognized subsequent to invoicing. For multi-year agreements, we generally invoice customers annually at the beginning of each annual coverage period. We record a receivable related to revenue recognized for multi-year on-premises licenses as we have an unconditional right to invoice and receive payment in the future related to those licenses.\nAs of June 30, 2019 and 2018, long-term accounts receivable, net of allowance for doubtful accounts, was $2.2 billion and $1.8 billion, respectively, and is included in other long-term assets in our consolidated balance sheets.\nThe allowance for doubtful accounts reflects our best estimate of probable losses inherent in the accounts receivable balance. We determine the allowance based on known troubled accounts, historical experience, and other currently available evidence.\nActivity in the allowance for doubtful accounts was as follows:", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "5ce893d110be3ff0b2ef7e9794c22cbc", "source_table_id": "16d66c36e9b7633ddba1a029d6d2413e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:ad5fb75b151c6c6ef20fc77c0f913087", "source": "tatqa", "table_key": "b1268d009bd571534e302a73efcfbfbecb81860a", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Domestic", "$(20,597)", "$22,325", "$29,088"], ["Foreign", "87,791", "150,051", "169,103"], ["Total", "$67,194", "$172,376", "$198,191"]]}, "native_split": "test", "question": "What was the percentage change in total pretax income between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "-61.02", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "ADVANCED ENERGY INDUSTRIES, INC. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS – (continued) (in thousands, except per share amounts)\nNOTE 5. INCOME TAXES\nThe geographic distribution of pretax income from continuing operations is as follows:", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "ad5fb75b151c6c6ef20fc77c0f913087", "source_table_id": "ea8b291b240fab467592f64e1d073957"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:557c1e6b072d90960071e39e584e404e", "source": "tatqa", "table_key": "0bf34fd9ac3b991e1dc86cc5c219113146e4ae2d", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["(in millions)", "2019", "2018", "2017"], ["Beginning balance of unrecognized tax benefits", "$ 356.4", "$ 214.3", "$ 162.6"], ["Additions for tax positions of current year", "3.4", "106.0", "7.3"], ["Additions for tax positions of prior years", "47.9", "59.5", "49.3"], ["Reductions for tax positions of prior years", "(16.0)", "(7.0)", "(4.3)"], ["Reductions for lapses of statutes of limitation and settlements", "(1.4)", "(16.4)", "(0.6)"], ["Ending balance of unrecognized tax benefits", "$ 390.3", "$ 356.4", "$ 214.3"]]}, "native_split": "test", "question": "What is the average annual Ending balance of unrecognized tax benefits? ", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "320.33", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Unrecognized Tax Benefits\nWe are providing the following disclosures related to our unrecognized tax benefits and the effect on our effective income tax rate if recognized:\nIn 2019, our unrecognized tax benefit increased by $33.9 million, primarily related to increases in North America. In 2018, we increased our unrecognized tax benefit by $142.1 million, also primarily related to North America.\nIf the unrecognized tax benefits at December 31, 2019 were recognized, our income tax provision would decrease by $343.5 million, resulting in a substantially lower effective tax rate. Based on the potential outcome of the Company’s global tax examinations and the expiration of the statute of limitations for specific jurisdictions, it is possible that the unrecognized tax benefits could change significantly within the next 12 months. Absent resolution of significant tax controversy, the associated impact on the reserve balance is estimated to be a decrease in the range of $4.6 to $6.6 million during 2020.\nWe recognize interest and penalties associated with unrecognized tax benefits in our income tax provision in the Consolidated Statements of Operations. Interest and penalties recorded were $13.1 million, negligible and $4.0 million, respectively in 2019, 2018 and 2017. We had gross liabilities, for interest and penalties, of $56.2 million at December 31, 2019, $18.2 million at December 31, 2018 and $14.8 million at December 31, 2017. The increase in the gross liability related to interest and penalties from 2018 to 2019 was primarily due to a reclass within other non-current liabilities from unrecognized tax benefits to interest and penalties which had no impact on the overall Consolidated Balance Sheets or Consolidated Statement of Operations.\nThe majority of the unrecognized tax benefit amount of $390.3 million relates to North America.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "557c1e6b072d90960071e39e584e404e", "source_table_id": "1aaa46cacf4e0bfb632992509c25e0b1"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:a0c0e850ec1d892b90e8b475c7ce0c8f", "source": "tatqa", "table_key": "73ebe7ace94d9e331f55fbd38bc9e0b78580d472", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Deferred tax assets:", "", ""], ["Employee benefits", "$5,092", "$5,078"], ["Inventory", "1,649", "1,129"], ["Accounts receivable", "204", "213"], ["Tax credits", "1,300", "1,213"], ["Other assets", "148", "139"], ["Capital Loss carry-forward", "2,455", "1,385"], ["Net operating loss carry-forwards", "5,556", "6,451"], ["Total deferred tax asset", "16,404", "15,608"], ["Deferred tax liabilities:", "", ""], ["Property, plant and equipment", "(1,344 )", "(1,639)"], ["Other liabilities", "(343 )", "(821)"], ["Deferred state income tax", "(767 )", "(727)"], ["Net deferred tax asset", "13,950", "12,421"], ["Valuation allowance", "(13,950 )", "(12,688)"], ["Net deferred tax liability", "$-", "$ (267)"]]}, "native_split": "test", "question": "What is the average value of employee benefits for 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "5085", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "The components of deferred taxes are as follows (in thousands):\nIn assessing the realizability of deferred tax assets, the Company considers whether it is more-likely-than-not that some portion or all of the deferred tax assets will be realized. A valuation allowance, if needed, reduces the deferred tax assets to the amounts expected to be realized. The ultimate realization of deferred tax assets is dependent upon the generation of future taxable income in those periods in which temporary differences become deductible and/or net operating loss carry-forwards can be utilized. We assess all positive and negative evidence when determining the amount of the net deferred tax assets that are more likely than not to be realized. This evidence includes, but is not limited to, prior earnings history, scheduled reversal of taxable temporary differences, tax planning strategies and projected future taxable income. Significant weight is given to positive and negative evidence that is objectively verifiable.\nAs required by the authoritative accounting guidance on accounting for income taxes, the Company evaluates the realizability of its deferred tax assets at each reporting date. Accounting for income taxes requires that a valuation allowance be established when it is more-likely-than-not that all or a portion of the deferred tax assets will not be realized. In circumstances where there is sufficient negative evidence indicating that the deferred tax assets are not more-likely-than-not realizable, the Company establishes a valuation allowance. As of April 30, 2019 and 2018, the Company had a full valuation allowance against its U.S. net deferred tax assets. If these estimates and assumptions change in the future, the Company may be required to reduce its existing valuation allowance resulting in less income tax expense. For the years ended April 30, 2019 and 2018, the valuation allowance increased by approximately $1.3 million and $9.4 million, respectively.\nAs of April 30, 2019, the Company has U.S. federal net operating losses of $23 million of which $4 million begins to expire in Fiscal 2023 through 2031 and which are subject to annual limitation under Internal Revenue Code Section 382. The remaining U.S. federal net operating losses of $18.9 million have an indefinite carry-forward period. The U.S. federal capital loss carry-forward of $9.9 million expires in 2023. The Company also has state net operating loss carry-forwards, R&D tax credits, and state tax credits that expire in various years and amounts.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "a0c0e850ec1d892b90e8b475c7ce0c8f", "source_table_id": "2d3af3804b56b1097f4ee95efbd4a598"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:1017e47aff2b4a3ddaab11eeabca5495", "source": "tatqa", "table_key": "e8649aebb8b7f4ebda2d950cf07edf96d6dd13a8", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended", ""], ["", "", "March 31,", ""], ["", "2019", "2018", "2017"], ["Contract manufacturers and consignment warehouses:       ", "", "", ""], ["Flextronics Technology", "21.8%", "14.0%", "10.4%"], ["Sanmina", "17.7", "16.0", "20.4"], ["Distributors:       ", "", "", ""], ["Avnet Logistics", "31.3", "35.3", "25.5"], ["Nexcomm ", "14.8", "16.1", "19.7"]]}, "native_split": "train", "question": "What was the average revenues from Avnet Logistics between 2017-2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "30.7", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "The following direct customers accounted for 10% or more of our net revenues in one or more of the following periods:\nNokia was our largest customer in fiscal 2019, 2018 and 2017. Nokia purchases products directly from us and through contract manufacturers and distributors. Based on information provided to us by its contract manufacturers and our distributors, purchases by Nokia represented approximately 45%, 36% and 41% of our net revenues in fiscal 2019, 2018 and 2017, respectively. To our knowledge, none of our other OEM customers accounted for more than 10% of our net revenues in any of these periods.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "1017e47aff2b4a3ddaab11eeabca5495", "source_table_id": "414c46230d16f755bb66ffddd864bd63"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:b2404881a9663f3dbef31778031d256f", "source": "tatqa", "table_key": "17d5431ea973f3b85bd3c944f843857c900d774f", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["", "", "", "", "Year Ended December 31,", ""], ["(In millions, unaudited)", "2019", "2018", "2017", "2016", "2015"], ["Net income (loss)", "$ (20.0)", "$ (50.5)", "$ 65.3", "$ 15.2", "$ (0.7)"], ["Add (subtract):", "", "", "", "", ""], ["Interest expense, net of interest income", "136.7", "134.5", "129.8", "76.8", "79.6"], ["Income tax expense (benefit)", "(3.7)", "(24.1)", "(124.9)", "23.0", "2.8"], ["Depreciation and amortization", "381.2", "432.6", "291.8", "174.0", "179.9"], ["EBITDA", "494.2", "492.5", "362.0", "289.0", "261.6"], ["Adjustments to EBITDA:", "", "", "", "", ""], ["Other, net (a)", "(8.8)", "0.6", "19.3", "(25.5)", "(22.3)"], ["Investment distributions (b)", "35.8", "39.1", "30.0", "32.1", "45.3"], ["(Gain) loss on extinguishment of debt (c)", "(4.5)", "—", "—", "6.6", "41.2"], ["Loss on impairment (d)", "—", "—", "—", "0.6", "—"], ["Non-cash, stock-based compensation (e)", "6.8", "5.1", "2.8", "3.0", "3.1"], ["Adjusted EBITDA", "$ 523.5", "$ 537.3", "$ 414.1", "$ 305.8", "$ 328.9"]]}, "native_split": "test", "question": "What is the increase/ (decrease) in Income tax expense (benefit) from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "20.4", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "In addition to the results reported in accordance with accounting principles generally accepted in the United States (“US GAAP” or “GAAP”), we also use certain non-GAAP measures such as EBITDA and adjusted EBITDA to evaluate operating performance and to facilitate the comparison of our historical results and trends.\nThese financial measures are not a measure of financial performance under US GAAP and should not be considered in isolation or as a substitute for net income (loss) as a measure of performance and net cash provided by operating activities as a measure of liquidity.\nEBITDA is defined as net earnings before interest expense, income taxes, and depreciation and amortization. Adjusted EBITDA is comprised of EBITDA, adjusted for certain items as permitted or required under our credit facility as described in the reconciliations below.\nThese measures are a common measure of operating performance in the telecommunications industry and are useful, with other data, as a means to evaluate our ability to fund our estimated uses of cash. The following tables are a reconciliation of net income (loss) to Adjusted EBITDA:\nThe following tables are a reconciliation of net income (loss) to Adjusted EBITDA:\n(a) Other, net includes the equity earnings from our investments, dividend income, income attributable to noncontrolling interests in subsidiaries, acquisition and transaction related costs including severance, non-cash pension and postretirement benefits and certain other miscellaneous items.\n(b) Includes all cash dividends and other cash distributions received from our investments.\n(c) Represents the redemption premium (discount) and write-off of unamortized debt issuance costs in connection with the redemption or retirement of our debt obligations.\n(d) Represents intangible asset impairment charges recognized during the period.\n(e) Represents compensation expenses in connection with the issuance of stock awards, which because of their non-cash nature, these expenses are excluded from adjusted EBITDA.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 10, "source_local_id": "b2404881a9663f3dbef31778031d256f", "source_table_id": "06f4135ecdf57596ac473ab26bbe4b21"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:5cd4cd363c7d32b54e62eef9dff81921", "source": "tatqa", "table_key": "58f53ecbcef7fc7d44b89b960d63af42fad1c9ed", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["€ million", "30/9/2018", "30/9/2019"], ["Provisions for post-employment benefits plans (employer’s commitments)", "344", "414"], ["Provisions for indirect commitments", "12", "17"], ["Provisions for voluntary pension benefits", "0", "0"], ["Provisions for post-employment benefit plans", "71", "78"], ["", "427", "509"], ["Provisions for obligations similar to pensions", "41", "34"], ["", "468", "543"]]}, "native_split": "test", "question": "What was the change in Provisions for post-employment benefits plans (employer’s commitments) in FY2019 from FY2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "70", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "32. Provisions for post-employment benefits plans and similar obligations\nProvisions for post-employment benefits plans are recognised in accordance with IAS 19 (Employee Benefits).\nProvisions for post-employment benefits plans consist of commitments primarily related to benefits defined by the provisions of company pension plans. These take the form of defined benefit plans directly from the employer (employer’s commitments) and defined benefit plans from external pension providers (benevolent funds in Germany and international pension funds). The external providers’ assets serve exclusively to finance the pension entitlements and qualify as plan assets. The benefits under the different plans are based on performance and length of service.\nThe most important performance-based pension plans are described in the following.", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "5cd4cd363c7d32b54e62eef9dff81921", "source_table_id": "832f8af1730c8f79e9d70fc20c3e9ed5"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:06b7c932aca209bc195ef9c2f6b8abb0", "source": "tatqa", "table_key": "6d9fefbae438fd066278f5d2c9ab18395ca04ded", "grid": {"TD": 1, "LD": 0, "nrows": 19, "ncols": 4, "section_rows": [], "cells": [["2018 FULL-YEAR RESULTS COMPARED TO 2017", "", "", ""], ["", "Years ended December 31", "Years ended December 31", "Years ended December 31"], ["(In millions of dollars, except margins)", "2018 1", "2017 1", "%Chg"], ["Revenue", "", "", ""], ["Wireless", "9,200", "8,569", "7"], ["Cable", "3,932", "3,894", "1"], ["Media", "2,168", "2,153", "1"], ["Corporate items and intercompany eliminations 2", "(204)", "(247)", "(17"], ["Revenue", "15,096", "14,369", "5"], ["Total service revenue 2", "12,974", "12,550", "3"], ["Adjusted EBITDA 3", "", "", ""], ["Wireless", "4,090", "3,726", "10"], ["Cable", "1,874", "1,819", "3"], ["Media", "196", "127", "54"], ["Corporate items and intercompany eliminations", "(177)", "(170)", "4"], ["Adjusted EBITDA 3", "5,983", "5,502", "9"], ["Adjusted EBITDA margin 3", "39.6%", "38.3%", ""], ["Net income", "2,059", "1,845", "12"], ["Adjusted net income 3", "2,241", "1,902", "18"]]}, "native_split": "test", "question": "What is the increase/ (decrease) in wireless revenue from 2017 to 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "631", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "1 Effective January 1, 2019, we adopted IFRS 16, with the ongoing impacts of this standard included in our results prospectively from that date. Our 2018 and 2017 results have not been restated for the effects of IFRS 16. See “Accounting Policies”. 2 As defined. See “Key Performance Indicators”.\n3 Adjusted EBITDA, adjusted EBITDA margin, and adjusted net income are non-GAAP measures and should not be considered substitutes or alternatives for GAAP measures. These are not defined terms under IFRS and do not have standard meanings, so may not be a reliable way to compare us to other companies. See “Non-GAAP Measures and Related Performance Measures” for information about these measures, including how we calculate them.\nRevenue Consolidated revenue increased by 5% in 2018, reflecting revenue growth of 7% in Wireless and 1% in both Cable and Media. Wireless revenue increased as a result of the increased mix of subscribers on higher-rate plans from our various brands and an increase in sales of higher-value devices.\nCable revenue increased by 1% as the increase in Internet revenue from the general movement of customers to higher speed and usage tiers of our Internet offerings was partially offset by the decrease in legacy Television subscribers and the impact of Phone pricing packages. Media revenue increased by 1% as a result of higher revenue at the Toronto Blue Jays, including a distribution from Major League Baseball, and higher Sportsnet and other network subscription revenue, partially offset by lower advertising revenue.\nAdjusted EBITDA Consolidated adjusted EBITDA increased in 2018 to $5,983 million, reflecting increases in Wireless, Cable, and Media. Wireless adjusted EBITDA increased 10% as a result of the strong flow-through of service revenue growth, partially offset by higher expenditures associated with increased subscriber volumes and costs of devices.\nCable adjusted EBITDA increased by 3% in 2018 as a result of strong Internet revenue growth, the ongoing product mix shift to higher-margin Internet services, and various cost efficiency and productivity initiatives. Media adjusted EBITDA increased 54% primarily as a result of the increase in revenue as discussed above and lower operating expenses from improvements made to our cost structure across the divisions.\nNet income and adjusted net income Net income and adjusted net income both increased in 2018 primarily as a result of higher adjusted EBITDA, partially offset by higher depreciation and amortization. Net income increased to $2,059 million in 2018 from $1,845 million in 2017 and adjusted net income increased to $2,241 million in 2018 from $1,902 million in 2017.\n2018 FULL-YEAR RESULTS COMPARED TO 2017", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 8, "source_local_id": "06b7c932aca209bc195ef9c2f6b8abb0", "source_table_id": "0e833c1fe5fb6e786cd372c7255eaf6e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:74a85951915329731485888e143aff54", "source": "tatqa", "table_key": "e8649aebb8b7f4ebda2d950cf07edf96d6dd13a8", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended", ""], ["", "", "March 31,", ""], ["", "2019", "2018", "2017"], ["Contract manufacturers and consignment warehouses:       ", "", "", ""], ["Flextronics Technology", "21.8%", "14.0%", "10.4%"], ["Sanmina", "17.7", "16.0", "20.4"], ["Distributors:       ", "", "", ""], ["Avnet Logistics", "31.3", "35.3", "25.5"], ["Nexcomm ", "14.8", "16.1", "19.7"]]}, "native_split": "train", "question": "What was the change in net revenue from Sanmina from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1.7", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "The following direct customers accounted for 10% or more of our net revenues in one or more of the following periods:\nNokia was our largest customer in fiscal 2019, 2018 and 2017. Nokia purchases products directly from us and through contract manufacturers and distributors. Based on information provided to us by its contract manufacturers and our distributors, purchases by Nokia represented approximately 45%, 36% and 41% of our net revenues in fiscal 2019, 2018 and 2017, respectively. To our knowledge, none of our other OEM customers accounted for more than 10% of our net revenues in any of these periods.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "74a85951915329731485888e143aff54", "source_table_id": "414c46230d16f755bb66ffddd864bd63"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:0535bc44c02c8cfb926982592cc6da8b", "source": "tatqa", "table_key": "de067bf6342f8143031a33c41441e60158be8c7e", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Parts and raw materials", "$134,816", "$76,647"], ["Work in process", "10,269", "6,644"], ["Finished goods", "84,934", "14,696"], ["Total", "$230,019", "$97,987"]]}, "native_split": "test", "question": "What was the percentage change in total inventories between 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "134.74", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "ADVANCED ENERGY INDUSTRIES, INC. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS – (continued) (in thousands, except per share amounts)\nNOTE 10. INVENTORIES\nOur inventories are valued at the lower of cost or net realizable value and computed on a first-in, first-out (FIFO) basis. Components of inventories are as follows:", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "0535bc44c02c8cfb926982592cc6da8b", "source_table_id": "d96d19db7d772ce4afe421dcda706842"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:a23b3dcb820703a1d9aea7dc33091afb", "source": "tatqa", "table_key": "8627e8b2465d5073a75b9ca689bcacbf8f97d0c9", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 3, "section_rows": [], "cells": [["", "Change between Fiscal increase (decrease)", ""], ["(In thousands)", "2019 and 2018", "2018 and 2017"], ["Payroll and payroll-related benefits", "$12,629", "$39,119"], ["Contract labour and consulting", "(6,791)", "(3,899)"], ["Share-based compensation", "(385)", "(1,490)"], ["Travel and communication", "(588)", "(343)"], ["Facilities", "(4,775)", "7,834"], ["Other miscellaneous", "(1,163)", "473"], ["Total change in research and development expenses", "$(1,073)", "$41,694"]]}, "native_split": "test", "question": "What is the difference between the Change between Fiscal increase for 2019 and 2018 and 2018 and 2017 for Payroll and payroll-related benefits?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "26490", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Research and development expenses consist primarily of payroll and payroll-related benefits expenses, contracted research and development expenses, and facility costs. Research and development assists with organic growth and improves product stability and functionality, and accordingly, we dedicate extensive efforts to update and upgrade our product offerings. The primary driver is typically budgeted software upgrades and software development.\nResearch and development expenses decreased by $1.1 million during the year ended June 30, 2019 as compared to the prior fiscal year. This was primarily due to a reduction in contract labour and consulting of $6.8 million and a reduction in the use of facility and related expenses of $4.8 million, partially offset by an increase in payroll and payroll-related benefits of $12.6 million. The increase in payroll and payroll-related benefits was driven primarily by increased headcount from recent acquisitions. Overall, our research and development expenses, as a percentage of total revenues, remained stable at approximately 11% compared to prior fiscal year.\nOur research and development labour resources increased by 336 employees, from 3,331 employees at June 30, 2018 to 3,667 employees at June 30, 2019.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "a23b3dcb820703a1d9aea7dc33091afb", "source_table_id": "d80ea6ee60a1b1f11e971791784d7d6c"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:8ddab4f696534c10763a19b55229e415", "source": "tatqa", "table_key": "fa17243151cffca948830ef0022e4c61444d5ad7", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 4, "section_rows": [], "cells": [["", "", "Pension", ""], ["", "2019", "2018", "2017"], ["Net periodic benefit cost:", "", "", ""], ["Expected long-term return on plan assets(1)", "3.6%", "3.8%", "3.3%"], ["Rate of compensation increase", "4.4%", "3.3%", "2.7%"], ["Discount rate", "2.2%", "2.1%", "1.9%"], ["Projected benefit obligation:", "", "", ""], ["Expected long-term return on plan assets", "2.0%", "3.6%", "4.0%"], ["Rate of compensation increase", "4.3%", "4.4%", "4.4%"], ["Discount rate(2)", "1.7%", "2.2%", "2.3%"]]}, "native_split": "test", "question": "What was the percentage change in the net periodic benefit cost rate of compensation increase between 2017 and 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0.6", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Assumptions\nWeighted-average actuarial assumptions used to determine net periodic benefit cost and projected benefit obligation for the plans for the fiscal years 2019, 2018 and 2017 were as follows:\n(1) The expected return on plan assets assumption used in calculating net periodic benefit cost is based on historical return experience and estimates of future long-term performance with consideration to the expected investment mix of the plan.\n(2) The discount rate is used to state expected cash flows relating to future benefits at a present value on the measurement date. This rate represents the market rate for high-quality fixed income investments whose timing would match the cash outflow of retirement benefits. Other assumptions include demographic factors such as retirement, mortality and turnover.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "8ddab4f696534c10763a19b55229e415", "source_table_id": "1a8874a4f3c3f8044c083509da0746b2"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:c10a228df13517c3f2312d1b281822f2", "source": "tatqa", "table_key": "63b72a3ad354e2812fc6d90c9e1e0d50bc6461d6", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 3, "section_rows": [], "cells": [["", "June 30,", ""], ["", "2019", "2018"], ["Deferred tax assets", "", ""], ["Non-capital loss carryforwards", "$161,119", "$129,436"], ["Capital loss carryforwards", "155", "417"], ["Undeducted scientific research and development expenses", "137,253", "123,114"], ["Depreciation and amortization", "683,777", "829,369"], ["Restructuring costs and other reserves", "17,845", "17,202"], ["Deferred revenue", "53,254", "62,726"], ["Other", "59,584", "57,461"], ["Total deferred tax asset", "$1,112,987", "$1,219,725"], ["Valuation Allowance", "$(77,328)", "$(80,924)"], ["Deferred tax liabilities", "", ""], ["Scientific research and development tax credits", "$(14,482)", "$(13,342)"], ["Other", "(72,599)", "(82,668)"], ["Deferred tax liabilities", "$(87,081)", "$(96,010)"], ["Net deferred tax asset", "$948,578", "$1,042,791"], ["Comprised of:", "", ""], ["Long-term assets", "1,004,450", "1,122,729"], ["Long-term liabilities", "(55,872)", "(79,938)"], ["", "$948,578", "$1,042,791"]]}, "native_split": "test", "question": "What is the percentage change of total assets from fiscal year 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "-9.03", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "The primary components of the deferred tax assets and liabilities are as follows, for the periods indicated below:\nWe believe that sufficient uncertainty exists regarding the realization of certain deferred tax assets that a valuation allowance is required. We continue to evaluate our taxable position quarterly and consider factors by taxing jurisdiction, including but not limited to factors such as estimated taxable income, any historical experience of losses for tax purposes and the future growth of OpenText.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "c10a228df13517c3f2312d1b281822f2", "source_table_id": "b3d63fb06110ad7e91c9e765227c1d27"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:43ce51bffe968258e499bfede2253d89", "source": "tatqa", "table_key": "6d9fefbae438fd066278f5d2c9ab18395ca04ded", "grid": {"TD": 1, "LD": 0, "nrows": 19, "ncols": 4, "section_rows": [], "cells": [["2018 FULL-YEAR RESULTS COMPARED TO 2017", "", "", ""], ["", "Years ended December 31", "Years ended December 31", "Years ended December 31"], ["(In millions of dollars, except margins)", "2018 1", "2017 1", "%Chg"], ["Revenue", "", "", ""], ["Wireless", "9,200", "8,569", "7"], ["Cable", "3,932", "3,894", "1"], ["Media", "2,168", "2,153", "1"], ["Corporate items and intercompany eliminations 2", "(204)", "(247)", "(17"], ["Revenue", "15,096", "14,369", "5"], ["Total service revenue 2", "12,974", "12,550", "3"], ["Adjusted EBITDA 3", "", "", ""], ["Wireless", "4,090", "3,726", "10"], ["Cable", "1,874", "1,819", "3"], ["Media", "196", "127", "54"], ["Corporate items and intercompany eliminations", "(177)", "(170)", "4"], ["Adjusted EBITDA 3", "5,983", "5,502", "9"], ["Adjusted EBITDA margin 3", "39.6%", "38.3%", ""], ["Net income", "2,059", "1,845", "12"], ["Adjusted net income 3", "2,241", "1,902", "18"]]}, "native_split": "test", "question": "What is the increase/ (decrease) in cable revenue from 2017 to 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "38", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "1 Effective January 1, 2019, we adopted IFRS 16, with the ongoing impacts of this standard included in our results prospectively from that date. Our 2018 and 2017 results have not been restated for the effects of IFRS 16. See “Accounting Policies”. 2 As defined. See “Key Performance Indicators”.\n3 Adjusted EBITDA, adjusted EBITDA margin, and adjusted net income are non-GAAP measures and should not be considered substitutes or alternatives for GAAP measures. These are not defined terms under IFRS and do not have standard meanings, so may not be a reliable way to compare us to other companies. See “Non-GAAP Measures and Related Performance Measures” for information about these measures, including how we calculate them.\nRevenue Consolidated revenue increased by 5% in 2018, reflecting revenue growth of 7% in Wireless and 1% in both Cable and Media. Wireless revenue increased as a result of the increased mix of subscribers on higher-rate plans from our various brands and an increase in sales of higher-value devices.\nCable revenue increased by 1% as the increase in Internet revenue from the general movement of customers to higher speed and usage tiers of our Internet offerings was partially offset by the decrease in legacy Television subscribers and the impact of Phone pricing packages. Media revenue increased by 1% as a result of higher revenue at the Toronto Blue Jays, including a distribution from Major League Baseball, and higher Sportsnet and other network subscription revenue, partially offset by lower advertising revenue.\nAdjusted EBITDA Consolidated adjusted EBITDA increased in 2018 to $5,983 million, reflecting increases in Wireless, Cable, and Media. Wireless adjusted EBITDA increased 10% as a result of the strong flow-through of service revenue growth, partially offset by higher expenditures associated with increased subscriber volumes and costs of devices.\nCable adjusted EBITDA increased by 3% in 2018 as a result of strong Internet revenue growth, the ongoing product mix shift to higher-margin Internet services, and various cost efficiency and productivity initiatives. Media adjusted EBITDA increased 54% primarily as a result of the increase in revenue as discussed above and lower operating expenses from improvements made to our cost structure across the divisions.\nNet income and adjusted net income Net income and adjusted net income both increased in 2018 primarily as a result of higher adjusted EBITDA, partially offset by higher depreciation and amortization. Net income increased to $2,059 million in 2018 from $1,845 million in 2017 and adjusted net income increased to $2,241 million in 2018 from $1,902 million in 2017.\n2018 FULL-YEAR RESULTS COMPARED TO 2017", "scale": "million", "scoring": "tolerance", "meta": {"paragraph_count": 8, "source_local_id": "43ce51bffe968258e499bfede2253d89", "source_table_id": "0e833c1fe5fb6e786cd372c7255eaf6e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:e602e37b68c7bd220e11cc8e92443e1c", "source": "tatqa", "table_key": "4aecedefce420f98111ba44669fb1cd174120a90", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended June 30,", ""], ["", "2019", "2018", "2017"], ["", "", "(U.S. $ in thousands)", ""], ["Executive management", "", "", ""], ["Short-term compensation and benefits", "$3,835", "$2,991", "$2,860"], ["Post-employment benefits", "109", "99", "100"], ["Share-based payments", "17,144", "9,335", "26,030"], ["", "$21,088", "$12,425", "$28,990"], ["Board of directors", "", "", ""], ["Cash remuneration", "$430", "$362", "$388"], ["Share-based payments", "1,772", "1,577", "1,825"], ["", "$2,202", "$1,939", "$2,213"]]}, "native_split": "test", "question": "What is the average annual total compensation for the Board of directors in fiscal years ended June 30, 2017, 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2118", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "19. Related Party Transactions\nKey management personnel compensation\nAll directors and executive management have authority and responsibility for planning, directing and controlling the activities of the Group, and are considered to be key management personnel.\nCompensation for the Group’s key management personnel is as follows:", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "e602e37b68c7bd220e11cc8e92443e1c", "source_table_id": "4bbe0a4d66c239a61db4574c14b7374c"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:98251769ea613235ffdac57a28787fc8", "source": "tatqa", "table_key": "68e649b9cf279ea2c5557df65065428cf6e594b4", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Balance at January 1", "$1,402", "$1,271", "$276"], ["Increases for tax positions related to the current year", "—", "131", "995"], ["Decreases for tax positions related to the current year", "(1,402)", "—", "—"], ["Balance at December 31", "$—", "$1,402", "$1,271"]]}, "native_split": "test", "question": "What is the change in balance between 2017 and 2018 at January 1?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "995", "answer_type": "arithmetic", "answer_from": "table-text", "paragraphs": "A reconciliation of the beginning and ending amount of unrecognized tax benefits is as follows (in thousands):\nThe provision for uncertain tax positions relates to business in territories outside of the US.\nThe Company’s policy is to classify interest and penalties on uncertain tax positions as a component of tax expense. The Company does not expect the change in uncertain tax positions to have a material impact on its financial position, results of operations, or liquidity.\nThe Company is subject to US federal income tax as well as to income tax in multiple state and foreign jurisdictions, including the UK. Federal income tax returns of the Company are subject to IRS examination for the 2016 through 2019 tax years. State income tax returns are subject to examination for the 2015 through 2019 tax years.\nCurrently, an audit is occurring in the United Kingdom for the year ended December 31, 2017. There are no ongoing audits in any other significant foreign tax jurisdictions.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "98251769ea613235ffdac57a28787fc8", "source_table_id": "5f0466453acb53d17eee77ec763d9a98"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:b33d187349988b3f56b048ac49529657", "source": "tatqa", "table_key": "c05fc04812945372c30fbde1e2df1c6d7b8db771", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["Region", "2017", "2018", "2019"], ["", "%", "%", "%"], ["Taiwan", "32.8", "36.4", "36.4"], ["Singapore", "20.6", "16.4", "16.2"], ["China (including Hong Kong)", "12.7", "12.2", "12.9"], ["Japan", "3.2", "3.9", "6.6"], ["USA", "12.2", "15.6", "13.5"], ["Europe", "9.6", "8.3", "4.7"], ["Others", "8.9", "7.2", "9.7"], ["Total", "100.0", "100.0", "100.0"]]}, "native_split": "test", "question": "What is the average customers from Singapore?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "17.73", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Customers and Markets\nOur primary customers, in terms of our sales revenues, include premier integrated device manufacturers, such as Texas Instruments and Intel Mobile, plus leading fabless design companies, such as Broadcom, MediaTek, Realtek, Qualcomm and Novatek. Although we are not dependent on any single customer, a significant portion of our operating revenues has been generated from sales to a few customers. Our top ten customers accounted for approximately 51.3% of our operating revenues in 2019. Set forth below is a geographic breakdown of our operating revenues in 2017, 2018 and 2019 by the location of our customers.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "b33d187349988b3f56b048ac49529657", "source_table_id": "8938cb551b520e6b84aaf1c047194e0f"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:4e2bf059f5e963012857a1f219110c13", "source": "tatqa", "table_key": "f9cf9bf922dd1ad80b9c8a82fa276f8aa55ed2eb", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 3, "section_rows": [], "cells": [["", "Consolidated", ""], ["", "2019", "2018"], ["", "US$000", "US$000"], ["Software license revenue", "82,575", "64,420"], ["Subscription and maintenance revenue", "64,955", "56,996"], ["Search advertising revenue", "17,940", "11,968"], ["Service revenue", "3,655", "5,532"], ["Other revenue", "2,694", "1,260"], ["", "171,819", "140,176"], ["Interest income", "933", "192"], ["Revenue", "172,752", "140,368"]]}, "native_split": "test", "question": "What is the percentage change in the software license revenue from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "28.18", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Note 3. Revenue\nAccounting policy for revenue recognition\nAASB 15 Revenue from Contracts with Customers establishes a comprehensive framework for determining the quantum and timing of revenue recognition. The AASB equivalent of IFRS 15 Revenue from Contract with Customers replaced IAS 18 Revenue, IAS 11 Construction Contracts and related interpretations.\nThe new standard is based on the principle that revenue is recognised when control of a good or service transfers to a customer – either over time or at a point in time – depending on when performance obligations are satisfied.\nThe Group has applied the new standard on 1 July 2018 using the modified retrospective approach with changes reflected in opening retained profits. The application of the standard did not result in a significant change to the recognition of revenue compared to the previous accounting policy for revenue.\nAltium has one performance obligation for each of the revenue streams listed below and has applied the following revenue recognition methods:\n1. Software licenses: Revenue is recognised at a point in time when license activation is available to the user. 2. Subscription and maintenance: Revenue is deferred and is subsequently recognised over the period in which the subscription service is provided. As the billing structure for customers is often bundled with licenses and billed on activation, there is an allocation methodology applied based on stand-alone selling prices to calculate the portion of revenue to be deferred. 3. Search advertising: Revenue is recognised at a point in time on a price-per-click basis, this is when a user engages with the search result on the website by clicking on it. 4. Services revenue: Revenue from providing services is recognised over the period in which the services are rendered. Services include training and implementation services. 5. Other revenue - Royalties: Royalties related to IP are recognised at a point in time when the subsequent sales occurs. 6. Interest income: Revenue is recognised on a time proportion basis, by reference to the principal outstanding and the effective interest rate applicable, which is the rate that discounts estimated future cash receipts through the expected life of the financial asset to the assets net carrying amount.\nImpact on opening retained profits\nFor incremental costs incurred in obtaining a contract, such as sales commissions, Altium has chosen to apply the practical expedient available under the standard which permits immediate expensing when the underlying asset is amortised in one year or less, given subscription periods are typically for a 12 month period.\nWhere revenue is deferred for more than 12 months and an upfront commission has been paid, the commission is capitalised and amortised over the period the revenue is recognised. Altium had US$6 million in long-term deferred revenue as at 30 June 2018 which resulted in an adjustment of US$0.3 million to opening retained profits on adoption of the standard.\nAltium had US$6.9 million in long-term deferred revenue as at June 2019 which resulted in an adjustment of US$ 0.2 million to capitalise commissions.\nCritical accounting judgements, estimates and assumptions\nRevenue for multiple element contracts is allocated based on stand-alone selling prices and then recognised revenue according to the accounting policy for each revenue stream.", "scale": "percent", "scoring": "tolerance", "meta": {"paragraph_count": 13, "source_local_id": "4e2bf059f5e963012857a1f219110c13", "source_table_id": "ce2ea049e35bd18c190d6f5676ec7d3d"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:4c309d1eb940630e20f4996998ab95bd", "source": "tatqa", "table_key": "63b91286b8bbd58c06a89de4d31288d26a5b5bf8", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "For the Year Ended December 31,", "", "Change Amount"], ["", "2018", "2017", ""], ["", "(dollars in thousands)", "", ""], ["Goodwill impairment and restructuring charges:", "", "", ""], ["Restructuring charges", "$ -", "$752", "$(752)"], ["Goodwill impairment", "9,174", "8,418", "756"], ["Total goodwill impairment and restructuring charges", "$9,174", "$9,170", "$4"]]}, "native_split": "test", "question": "What is the total goodwill impairment and restructuring charges in 2017 and 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "18344", "answer_type": "arithmetic", "answer_from": "table", "paragraphs": "Goodwill Impairment and Restructuring Charges\nA goodwill impairment charge of $9.2 million was recorded in 2018 and a $8.4 million goodwill impairment charge was recorded in 2017, both of which related to our Brazilian operations (see Note 7 of the notes to our consolidated financial statements). There were no restructuring charges incurred in the year ended December 31, 2018, compared to $0.8 million incurred in the year ended December 31, 2017. We incurred restructuring charges in the third quarter of 2017 associated with the closure of our Overland Park office, including termination benefits and other reorganization costs, primarily associated with integrating operations.", "scale": "thousand", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "4c309d1eb940630e20f4996998ab95bd", "source_table_id": "57edd1f11a1aacefc20eea07b10ff60e"}, "dataset": "tatqa", "subtype": "arithmetic"}
{"id": "tatqa:941d5dd463779070499ea058ec309fdb", "source": "tatqa", "table_key": "67229f41f8af637828c95de47f4854ac5b16715e", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 5, "section_rows": [], "cells": [["", "", "Fiscal Year Ended September 28, 2019", "", ""], ["", "", "Reportable Segment:", "", ""], ["", "AMER ", "APAC ", "EMEA ", "Total"], ["Market Sector:", "", "", "", ""], ["Healthcare/Life Sciences ", "$488,851", "$602,922", "$128,225", "$1,219,998"], ["Industrial/Commercial ", "359,381", "534,971", "86,868", "981,220"], ["Aerospace/Defense ", "317,558", "186,486", "84,556", "588,600"], ["Communications ", "256,523", "113,329", "4,764", "374,616"], ["     External revenue ", "1,422,313", "1,437,708", "304,413", "3,164,434"], ["Inter-segment sales ", "6,995", "119,497", "5,520", "132,012"], ["    Segment revenue ", "$1,429,308", "$1,557,205", "$309,933", "$3,296,446"]]}, "native_split": "test", "question": "How many geographical segments did external revenue exceed $1,000,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Disaggregated Revenue\nThe table below includes the Company’s revenue for the fiscal year ended September 28, 2019 disaggregated by geographic reportable segment and market sector (in thousands):", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "941d5dd463779070499ea058ec309fdb", "source_table_id": "39b64d6ba35c2776887244237e544c22"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:d36fbc7164815665e51507e087e10e21", "source": "tatqa", "table_key": "0c438343c4ac662ad5446b61cbfb9b33c06e96c7", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "Year Ended", "", ""], ["", "June 30, 2019", "June 30, 2018", "June 30, 2017"], ["Domestic ", "$22,330", "$(55,197)", "$(7,228)"], ["Foreign  ", "(48,204)", "8,550", "9,824"], ["Total ", "$(25,874)", "$(46,647)", "$2,596"]]}, "native_split": "test", "question": "How many years did Foreign income exceed $5,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "15. Income Taxes\nIncome before income taxes is as follows (in thousands):", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "d36fbc7164815665e51507e087e10e21", "source_table_id": "6406863d7dd28b35452d7fda63aee1dc"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:7822c08381f47d253e30e315b8c7cf03", "source": "tatqa", "table_key": "820b4b2bedbfacce6266b68ca48f598a5eae2241", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Land", "$11.9", "$11.9"], ["Buildings and improvements", "81.2", "71.1"], ["Leasehold improvements", "7.1", "6.7"], ["Computer equipment", "234.1", "208.9"], ["Furniture, fixtures and other equipment", "11.2", "11.0"], ["Property and equipment", "345.5", "309.6"], ["Accumulated depreciation and amortization", "(168.6)", "(132.5)"], ["Property and equipment, net", "$176.9", "$177.1"]]}, "native_split": "test", "question": "How many years did furniture, fixtures and other equipment exceed $10 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "(7) Property and Equipment\nProperty and equipment consist of the following (in millions):\nOn December 31, 2019, we entered into finance lease agreements for certain computer equipment. The leased equipment was valued at $13.7 million, net of prepaid maintenance and $0.3 million of imputed interest, and is included in Property and equipment, net on the Consolidated Balance Sheets. Refer to Note 12 — Long-Term Debt for additional information related to our finance leases.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "7822c08381f47d253e30e315b8c7cf03", "source_table_id": "4c831b52efd5a0200d3f85447d6437a3"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:4a1c277abf7bb1e3a8683e0ebab8f891", "source": "tatqa", "table_key": "1a4f9c696cf4743bc5e214ff722638732f35af40", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 5, "section_rows": [], "cells": [["Fiscal 2020", "Q1 2020 (1)", "Q2 2020 (1)", "Q3 2020 (1)", "Q4 2020"], ["Total revenue", "$2,450", "$2,632", "$2,656", "$3,073"], ["Net income attributable to VMware, Inc.", "380", "5,303", "407", "321"], ["common stockholders, basic for Classes A and B Net income per weighted-average share attributable to VMware, Inc. common stockholders, basic for Classes A and B", "$0.91", "$12.72", "$0.98", "$0.77"], ["Net income per weighted-average share attributable to VMware, Inc. common stockholders, diluted for Classes A and B", "$0.89", "$12.47", "$0.96", "$0.76"]]}, "native_split": "test", "question": "How many quarters did Net income attributable to VMware, Inc. exceed $1,000 million in 2020?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "S. Selected Quarterly Financial Data (unaudited)\nQuarterly financial data for fiscal 2020 and 2019 were as follows (tables in millions, except per share amounts):\n(1) Adjusted to reflect the recast of prior period information due to the Pivotal acquisition, which was accounted for as a transaction between entities under common control (refer to Note B).", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "4a1c277abf7bb1e3a8683e0ebab8f891", "source_table_id": "24e23b5428ac97c8ae3cca76f5382437"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:e54e1bb1dd327e05de4bf3514bfd7128", "source": "tatqa", "table_key": "55e8298c0ae8ab0bb23452d1c798e8f87b7e5815", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Transaction processing liabilities", "$24,465", "$4,958"], ["Servicing liabilities(1)", "3,796", "3,016"], ["Distributions payable(2)", "5,978", "10,066"], ["Interest rate swap(3)", "2,763", "—"], ["Tax related liabilities(4)", "873", "4,412"], ["Deferred lease liabilities(5)", "—", "2,489"], ["Accruals and other liabilities(6)", "9,442", "10,110"], ["Other liabilities", "$47,317", "$35,051"]]}, "native_split": "test", "question": "How many years did servicing liabilities exceed $3,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "GreenSky, Inc. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS — (Continued) (United States Dollars in thousands, except per share data, unless otherwise stated)\nNote 9. Other Liabilities\nThe following table details the components of other liabilities in the Consolidated Balance Sheets as of the dates indicated.\n(1) We elected the fair value method to account for our servicing liabilities. Refer to Note 3 for additional information.\n(2) Related party distributions payable are not included in this balance, but rather are included within related party liabilities.\n(3) Refer to Note 3 and Note 8 for additional information on our interest rate swap, which was in a liability position as of December 31, 2019.\n(4) Tax related liabilities primarily include certain taxes payable related to the Reorganization Transactions.\n(5) Deferred lease liabilities were calculated in accordance with legacy lease guidance in ASC 840, Leases, for the amount presented as of December 31, 2018. Under the new lease guidance codified in ASC 842, Leases, which we adopted on January 1, 2019, we presented operating lease liabilities separately on the Consolidated Balance Sheets as of December 31, 2019. See Note 1 and Note 14 for additional information on our lease accounting.\n(6) Accruals and other liabilities as of December 31, 2018 was adjusted to exclude the financial guarantee liability to conform to the current period presentation in the Consolidated Balance Sheets. Refer to Note 1 for additional discussion of our basis of presentation.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 9, "source_local_id": "e54e1bb1dd327e05de4bf3514bfd7128", "source_table_id": "8d2ef56b46f727dc18b63b78a4d26d02"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:ee4404eb53d219097da1c6dc6c4de31c", "source": "tatqa", "table_key": "7a878c4eb3435f958999b08c4b4decb993c018d2", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Depreciation expense", "$2,540", "$2,320", "$2,149"], ["Amortization expense", "4,764", "2,158", "1,834"], ["Impairment losses", "—", "19", "78"]]}, "native_split": "test", "question": "How many years did depreciation expense exceed $2,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table-text", "paragraphs": "GreenSky, Inc. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS — (Continued) (United States Dollars in thousands, except per share data, unless otherwise stated)\nNote 6. Property, Equipment and Software\nThe following table shows depreciation and amortization expense, as well as recorded impairment losses related to abandoned capitalized software projects that are recorded within general and administrative expense in the Consolidated Statements of Operations. We determined that these software projects would not generate future cash flows through use or disposal to a third party and, as such, the fair value as of the respective reporting dates was $0.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "ee4404eb53d219097da1c6dc6c4de31c", "source_table_id": "414d3b3dabd0898d3de089e2cc2d6bf5"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:6593c1ee1b47ca7d5e5dd1de12646598", "source": "tatqa", "table_key": "e4aa51e4f84dda97dfe8368851ca9519f4415dc8", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["Years Ended", "July 27, 2019", "July 28, 2018", "July 29, 2017"], ["Revenue:", "", "", ""], ["Infrastructure Platforms  .", "$30,191", "$28,322", "$27,817"], ["Applications .", "5,803", "5,036", "4,568"], ["Security", "2,730", "2,352", "2,152"], ["Other Products .", "281", "999", "1,168"], ["Total Product  .", "39,005", "36,709", "35,705"], ["Services", "12,899", "12,621", "12,300"], ["Total (1)", "$51,904", "$49,330", "$48,005"]]}, "native_split": "test", "question": "How many years did revenue from Other products exceed $1,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "(b) Revenue for Groups of Similar Products and Services\nWe design, manufacture, and sell IP-based networking and other products related to the communications and IT industry and provide services associated with these products and their use.\nThe following table presents revenue for groups of similar products and services (in millions):\n(1) Includes SPVSS business revenue of $168 million and $903 million for fiscal 2019 and 2018, respectively.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "6593c1ee1b47ca7d5e5dd1de12646598", "source_table_id": "d9929fa56778e739f071d0d586171994"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:10b200596b0327d61d587cd9a7b8fe64", "source": "tatqa", "table_key": "99ebcfeba0a659d93bbd043cc25725e30df6213b", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["USDm", "2019", "2018", "2017"], ["Reconciliation to net profit/(loss) for the year", "", "", ""], ["Net profit/(loss) for the year", "166.0", "-34.8", "2.4"], ["Reversal of impairment losses on tangible assets", "-120.0", "-", "-"], ["Net profit/(loss) for the year excluding impairment", "46.0", "-34.8", "2.4"]]}, "native_split": "test", "question": "How many years was there a positive net profit for the year excluding impairment recorded?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table", "paragraphs": "ALTERNATIVE PERFORMANCE MEASURES\nNet profit/(loss) for the year excluding impairment:\nNet profit excluding impairment is net profit less impairment and reversals of impairment generated from impairment testing during the year (Please refer to Note 8). The Company reports Net profit excluding impairment because we believe it provides additional meaningful information to investors regarding the operational performance excluding fluctuations in the valuation of fixed assets.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "10b200596b0327d61d587cd9a7b8fe64", "source_table_id": "bf323df09be1d8b4fbc8cc4df2936c92"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:635d756eb2458f9cb07ad2fc9ef5ce7a", "source": "tatqa", "table_key": "b0e749b6bebdac5aab2ff74bcb8b3beabc325261", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 6, "section_rows": [], "cells": [["Remuneration key performance indicator", "2019 actual", "2019 threshold", "2019 target", "2019 maximum", "Remuneration measure"], ["Group operating profit (£m)", "277.3", "256.7", "270.3", "283.8", "Annual Incentive Plan"], ["Group cash generation (£m)", "296.4", "270.7", "285.0", "299.2", "Annual Incentive Plan"], ["Group ROCE (%)", "54.5", "50.1", "52.7", "55.3", "Annual Incentive Plan"], ["2017-2019 EPS (%)", "57.5", "27.6", "N/A", "52.3", "Performance Share Plan"], ["2017-2019 relative TSR (percentile TSR)", "94th", "50th", "N/A", "75th", "Performance Share Plan"]]}, "native_split": "test", "question": "How many remuneration key performance indicators used Annual Incentive Plan as the remuneration measure?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table", "paragraphs": "5. Remuneration continued\nRemuneration at a glance 2019\nHow we performed\nThe above figures exclude the acquisition of Thermocoax.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "635d756eb2458f9cb07ad2fc9ef5ce7a", "source_table_id": "5e5f8b76df669b1a32df3074c2c551be"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:94d099d8cf03272c4750a76ec5e42b8c", "source": "tatqa", "table_key": "89a31eb1af35e3fd3c037908ad3e8d91f3d34c34", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["", "Bonds", "Bank loans", "Finance lease liabilities", "Interest payable", "Derivative financial instruments"], ["Group - 2018", "S$ Mil", "S$ Mil", "S$ Mil", "S$ Mil", "S$ Mil"], ["As at 1 April 2017", "8,726.6", "2,306.3", "198.2", "142.7", "(245.3)"], ["Financing cash flows (1)", "(506.2)", "222.6", "(28.3)", "(379.9)", "61.4"], ["Non-cash changes:", "", "", "", "", ""], ["Fair value adjustments", "(65.4)", "-", "-", "-", "107.8"], ["Amortisation of bond discount", "3.2", "-", "-", "-", "-"], ["Foreign exchange movements", "(273.3)", "(58.5)", "(0.5)", "(8.5)", "11.5"], ["Acquisition of subsidiary", "-", "31.3", "8.7", "-", ""], ["Additions of finance lease", "-", "-", "4.8", "-", "-"], ["Interest expense", "-", "-", "-", "383.6", "-"], ["Adjustment", "-", "-", "(78.3)", "-", "-"], ["", "(335.5)", "(27.2)", "(65.3)", "375.1", "119.3"], ["As at 31 March 2018", "7,884.9", "2,501.7", "104.6", "137.9", "(64.6)"]]}, "native_split": "test", "question": "How many categories of liabilities are there?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "5", "answer_type": "count", "answer_from": "table", "paragraphs": "30. RECONCILIATION OF LIABILITIES FROM FINANCING ACTIVITIES (Cont’d)\nNote: (1) The cash flows comprise the net amount of proceeds from borrowings and repayments of borrowings, net interest paid on borrowings, and settlement of swaps for bonds repaid in the statement of cash flows.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "94d099d8cf03272c4750a76ec5e42b8c", "source_table_id": "9acc7faf491d7a0ed461977c9e5cbcbb"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:9371120676b6497d7f014ad5c844ba57", "source": "tatqa", "table_key": "5dbd6eef3ba8ed70416ba12a295f0bd45a83a4f5", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31,2018"], ["Accounts receivable", "$68,309", "$76,376"], ["Less: provision for doubtful receivables", "(16,377)", "(16,086)"], ["Accounts receivable, net", "$51,932", "$60,290"]]}, "native_split": "test", "question": "How many years did Accounts receivable exceed $50,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nNOTE 5: ACCOUNTS RECEIVABLE, NET\nAccounts receivable consisted of the following:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "9371120676b6497d7f014ad5c844ba57", "source_table_id": "5208576a85192612b24f629769981aa8"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:3b1659ecae50b5da79d5ab902fea9447", "source": "tatqa", "table_key": "020667b07593a39dee6ca44c5a47b8b4372d6cbb", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["", "", "", "Payments due by period (in thousands)", "", ""], ["", "Total", "Less than 1 year", "1-3 years", "3-5 years", "After 5 years"], ["Notes payable and long-term debt", "$2,496,465", "$375,181", "$491,655", "$1,134,733", "$494,896"], ["Future interest on notes payable and long-term debt(1)", "373,762", "109,506", "142,082", "55,463", "66,711"], ["Operating lease obligations", "603,185", "118,312", "187,644", "114,297", "182,932"], ["Capital lease obligations", "77,829", "6,038", "11,726", "10,928", "49,137"], ["Non-cancelable purchase order obligations(2)", "351,230", "289,516", "61,537", "177", "—"], ["Pension and post retirement contributions and payments(3)", "14,618", "1,135", "1,904", "2,396", "9,183"], ["Other(4)", "77,669", "17,922", "27,863", "14,214", "17,670"], ["Total contractual obligations(5)", "$3,994,758", "$917,610", "$924,411", "$1,332,208", "$820,529"]]}, "native_split": "test", "question": "How many contractual obligations had a total payments due that exceeded $500,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table", "paragraphs": "Contractual Obligations\nOur contractual obligations as of August 31, 2019 are summarized below. As disclosed below, while we have certain non-cancelable purchase order obligations for property, plant and equipment, we generally do not enter into non-cancelable purchase orders for materials until we receive a corresponding purchase commitment from our customer. Non-cancelable purchase orders do not typically extend beyond the normal lead time of several weeks, at most. Purchase orders beyond this time frame are typically cancelable.\n(1) Consists of interest on notes payable and long-term debt outstanding as of August 31, 2019. Certain of our notes payable and long-term debt pay interest at variable rates. We have applied estimated interest rates to determine the value of these expected future interest payments.\n(2) Consists of purchase commitments entered into as of August 31, 2019 primarily for property, plant and equipment and software pursuant to legally enforceable and binding agreements.\n(3) Includes the estimated company contributions to funded pension plans during fiscal year 2020 and the expected benefit payments for unfunded pension and postretirement plans from fiscal years 2020 through 2029. These future payments are not recorded on the Consolidated Balance Sheets but will be recorded as incurred.\n(4) Includes (i) a $28.5 million capital commitment, (ii) a $16.2 million obligation related to a new human resource system and (iii) $33.0 million related to the one-time transition tax as a result of the Tax Act that will be paid in annual installments through fiscal year 2026.\n(5) As of August 31, 2019, we have $1.5 million and $103.7 million recorded as a current and a long-term liability, respectively, for uncertain tax positions. We are not able to reasonably estimate the timing of payments, or the amount by which our liability for these uncertain tax positions will increase or decrease over time, and accordingly, this liability has been excluded from the above table.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 7, "source_local_id": "3b1659ecae50b5da79d5ab902fea9447", "source_table_id": "fe0e8fe94bab06428abbca1372dc801f"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:41cad27df8a55d8e3200e8238bf41641", "source": "tatqa", "table_key": "5bafcb17d278169262817c5c01738ee2fcdd0556", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["", "", "Years Ended", "", "", ""], ["", "July 27, 2019", "July 28, 2018", "July 29, 2017", "Variance in Dollars", "Variance in Percent"], ["Research and development", "$ 6,577", "$ 6,332", "$6,059", "$245", "4%"], ["Percentage of revenue", "12.7%", "12.8%", "12.6%", "", ""], ["Sales and marketing", "9,571", "9,242", "9,184", "329", "4%"], ["Percentage of revenue", "18.4%", "18.7%", "19.1%", "", ""], ["General and administrative", "1,827", "2,144", "1,993", "(317)", "(15)%"], ["Percentage of revenue", "3.5%", "4.3%", "4.2%", "", ""], ["Total", "$17,975", "$17,718", "$17,236", "$257", "1%"], ["Percentage of revenue", "34.6%", "35.9%", "35.9%", "", ""]]}, "native_split": "test", "question": "How many years did general and administrative expense exceed $2,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Research and Development (“R&D”), Sales and Marketing, and General and Administrative (“G&A”) Expenses\nR&D, sales and marketing, and G&A expenses are summarized in the following table (in millions, except percentages):\nR&D Expenses R&D expenses increased due to higher headcount-related expenses and, to a lesser extent, higher acquisition-related costs, higher contracted services and higher discretionary spending.\nWe continue to invest in R&D in order to bring a broad range of products to market in a timely fashion. If we believe that we are unable to enter a particular market in a timely manner with internally developed products, we may purchase or license technology from other businesses, or we may partner with or acquire businesses as an alternative to internal R&D.\nSales and Marketing Expenses Sales and marketing expenses increased due to higher headcount-related expenses, higher discretionary spending and, to a lesser extent, higher contracted services and higher acquisition-related costs, partially offset by lower share-based compensation expense.\nG&A Expenses G&A expenses decreased due to a benefit from the $400 million litigation settlement with Arista Networks and lower contracted services, partially offset by higher discretionary spending and higher headcount-related expenses.\nEffect of Foreign Currency In fiscal 2019, foreign currency fluctuations, net of hedging, decreased the combined R&D, sales and marketing, and G&A expenses by approximately $233 million, or 1.3%, compared with fiscal 2018. In fiscal 2018, foreign currency fluctuations, net of hedging, increased the combined R&D, sales and marketing, and G&A expenses by approximately $93 million, or 0.5%, compared with fiscal 2017.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 7, "source_local_id": "41cad27df8a55d8e3200e8238bf41641", "source_table_id": "33295076b558d53b86fd6e5537022af6"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:81d29981f1dc165d704bfa76e41dd944", "source": "tatqa", "table_key": "cd17de5e22e72c18a256f3be278b60101eecd689", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31", ""], ["", "2019", "2018"], ["Total credit facility", "$300,000", "$300,000"], ["Balance outstanding", "$99,700", "$50,000"], ["Standby letters of credit", "$1,800", "$1,940"], ["Amount available", "$198,500", "$248,060"], ["Weighted-average interest rate", "3.25%", "3.10%"], ["Commitment fee percentage per annum", "0.23%", "0.20%"]]}, "native_split": "test", "question": "How many years did the Weighted-average interest rate exceed 3%?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nNOTE 12 — Debt\nLong-term debt was comprised of the following:\nOn February 12, 2019, we entered into an amended and restated five-year Credit Agreement with a group of banks (the \"Credit Agreement\") to extend the term of the facility. The Credit Agreement provides for a revolving credit facility of $300,000, which may be increased by $150,000 at the request of the Company, subject to the administrative agent's approval. This new unsecured credit facility replaces the prior $300,000 unsecured credit facility, which would have expired August 10, 2020. Borrowings of $50,000 under the prior credit agreement were refinanced into the Credit Agreement. The prior agreement was terminated as of February 12, 2019.\nThe Revolving Credit Facility includes a swing line sublimit of $15,000 and a letter of credit sublimit of $10,000. Borrowings under the Revolving Credit Facility bear interest at the base rate defined in the Credit Agreement. We also pay a quarterly commitment fee on the unused portion of the Revolving Credit Facility. The commitment fee ranges from 0.20% to 0.30% based on our total leverage ratio.\nThe Revolving Credit Facility requires, among other things, that we comply with a maximum total leverage ratio and a minimum fixed charge coverage ratio. Failure to comply with these covenants could reduce the borrowing availability under the Revolving Credit Facility. We were in compliance with all debt covenants at December 31, 2019. The Revolving Credit Facility requires that we deliver quarterly financial statements, annual financial statements, auditor certifications, and compliance certificates within a specified number of days after the end of a quarter and year. Additionally, the Revolving Credit Facility contains restrictions limiting our ability to: dispose of assets; incur certain additional debt; repay other debt or amend subordinated debt instruments; create liens on assets; make investments, loans or advances; make acquisitions or engage in mergers or consolidations; engage in certain transactions with our subsidiaries and affiliates; and make stock repurchases and dividend payments. Interest rates on the Revolving Credit Facility fluctuate based upon the LIBOR and the Company’s quarterly total leverage ratio.\nWe have debt issuance costs related to our long-term debt that are being amortized using the straight-line method over the life of the debt. Amortization expense for the twelve months ended December 31, 2019 was approximately $163 and $185 in 2018 and 2017. These costs are included in interest expense in our Consolidated Statement of Earnings.\nWe use interest rate swaps to convert the revolving credit facility's variable rate of interest into a fixed rate on a portion of the debt as described more fully in Note 13 \"Derivatives.\" These swaps are treated as cash flow hedges and consequently, the changes in fair value were recorded in other comprehensive earnings.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 8, "source_local_id": "81d29981f1dc165d704bfa76e41dd944", "source_table_id": "0dfb4455ae2e1b2066e99d9a3a05baf0"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:05eb4bf6b50c8375b2e6cf3416e4b419", "source": "tatqa", "table_key": "fe58560d19f5869eb18695c46cc94c62dae4e60c", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 5, "section_rows": [], "cells": [["", "For the Fiscal Years Ended", "", "", ""], ["", "March 31, 2018", "", "March 31, 2017", ""], ["", "Amount", "% to Net Sales", "Amount", "% to Net Sales"], ["Net sales (1)", "$201,977", "", "$182,228", ""], ["Segment operating income (loss) (1)", "3,622", "1.8%", "(9,028)", "(5.0)%"]]}, "native_split": "test", "question": "How many years did net sales exceed $200,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Film and Electrolytic\nThe table below sets forth net sales, operating income (loss) and operating income (loss) as a percentage of net sales for our Film and Electrolytic reportable segment for the fiscal years 2018 and 2017 (amounts in thousands, except percentages):\n(1) Fiscal years ending March 31, 2018 and 2017 adjusted due to the adoption of ASC 606\nNet Sales\nFilm and Electrolytic net sales of $202.0 million in fiscal year 2018 increased $19.7 million or 10.8% from $182.2 million in fiscal year 2017. The increase in net sales was primarily driven by an increase in net sales in the distributor channel across all the APAC and EMEA regions of $13.7 million, a $3.3 million increase in net sales in the OEM channel of the EMEA region and a $4.2 million increase in the EMS channel across the Americas, EMEA, and APAC regions. These increases were partially offset by a decrease in net sales of $1.2 million in the OEM channel across the Americas, APAC, and JPKO regions. In addition, there was a favorable impact of $7.6 million from foreign currency exchange primarily due to the change in the value of the Euro compared to the U.S. dollar.\nReportable Segment Operating Income (Loss)\nSegment operating income of $3.6 million in fiscal year 2018 improved $12.7 million from $9.0 million of operating loss in fiscal year 2017. The improvement was primarily attributable to a $4.3 million increase in gross margin driven by higher net sales, as well as the benefit of completed restructuring activities. The increase was also attributed to an $11.7 million improvement in (gain) loss on the write down and disposal of long-lived assets. These improvements were partially offset by a $2.1 million increase in restructuring charges, a $0.7 million increase in SG&A expenses, and a $0.6 million increase in R&D expenses.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 7, "source_local_id": "05eb4bf6b50c8375b2e6cf3416e4b419", "source_table_id": "23b98a2dfcb8b787448e527f00cffa9b"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:ffffbb2a89fa772045adbff4deae3699", "source": "tatqa", "table_key": "070471222de9c68f8bb37f9423cd386bb58ee343", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Trade receivables — billed", "$136.6", "$136.6"], ["Trade receivables — unbilled", "39.8", "37.0"], ["Trade receivables", "176.4", "173.6"], ["Allowance for doubtful accounts", "(1.3)", "(1.3)"], ["Trade receivables, net", "$175.1", "$172.3"]]}, "native_split": "test", "question": "How many years did Trade receivables, net exceed $150 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Trade Receivables, Net\nThe carrying amounts reported in the Consolidated Balance Sheets for Trade receivables, net approximate their fair value because of their short-term nature.\nA summary of Trade receivables, net of allowance for doubtful accounts is as follows (in millions):\nIn addition to the amounts above, we have unbilled receivables that we do not expect to collect within the next year included in Other non-current assets in our Consolidated Balance Sheets. Billings for these receivables are based on contractual terms. Refer to Note 11 — Other Non-Current Assets.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "ffffbb2a89fa772045adbff4deae3699", "source_table_id": "7bc6da25adaae100b2983a53464947c1"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:e6c8eef8ea37699e9d8c1f6b5f50b451", "source": "tatqa", "table_key": "aeaff78ee919100a5b5f75bb225bacbe113c4507", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "", "Fiscal 2019", "", "Fiscal 2018"], ["", "High", "Low", "High", "Low"], ["First Quarter", "$ 83.14", "$ 63.81", "$ 45.24", "$ 37.43"], ["Second Quarter", "$ 88.08", "$ 70.26", "$ 45.14", "$ 37.55"], ["Third Quarter", "$ 83.95", "$ 54.50", "$ 64.06", "$ 43.24"], ["Fourth Quarter", "$ 78.35", "$ 61.00", "$ 69.75", "$ 52.00"]]}, "native_split": "test", "question": "In 2019, how many quarters did the high share price exceed $85.00?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table", "paragraphs": "Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities\nThe Company’s common stock is traded on the NASDAQ Stock Market LLC (“NASDAQ”) under the symbol NTAP.\nPrice Range of Common Stock\nThe price range per share of common stock presented below represents the highest and lowest intraday sales prices for the Company’s common stock on the NASDAQ during each quarter of our two most recent fiscal years.\nHolders\nAs of June 7, 2019 there were 413 holders of record of our common stock.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "e6c8eef8ea37699e9d8c1f6b5f50b451", "source_table_id": "9082b05b2e0e2600d7f405f82795f041"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:b0b1580d6fd9fc014e30a5f20a25df66", "source": "tatqa", "table_key": "89a31eb1af35e3fd3c037908ad3e8d91f3d34c34", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["", "Bonds", "Bank loans", "Finance lease liabilities", "Interest payable", "Derivative financial instruments"], ["Group - 2018", "S$ Mil", "S$ Mil", "S$ Mil", "S$ Mil", "S$ Mil"], ["As at 1 April 2017", "8,726.6", "2,306.3", "198.2", "142.7", "(245.3)"], ["Financing cash flows (1)", "(506.2)", "222.6", "(28.3)", "(379.9)", "61.4"], ["Non-cash changes:", "", "", "", "", ""], ["Fair value adjustments", "(65.4)", "-", "-", "-", "107.8"], ["Amortisation of bond discount", "3.2", "-", "-", "-", "-"], ["Foreign exchange movements", "(273.3)", "(58.5)", "(0.5)", "(8.5)", "11.5"], ["Acquisition of subsidiary", "-", "31.3", "8.7", "-", ""], ["Additions of finance lease", "-", "-", "4.8", "-", "-"], ["Interest expense", "-", "-", "-", "383.6", "-"], ["Adjustment", "-", "-", "(78.3)", "-", "-"], ["", "(335.5)", "(27.2)", "(65.3)", "375.1", "119.3"], ["As at 31 March 2018", "7,884.9", "2,501.7", "104.6", "137.9", "(64.6)"]]}, "native_split": "test", "question": "How many types of non-cash changes are there?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "7", "answer_type": "count", "answer_from": "table", "paragraphs": "30. RECONCILIATION OF LIABILITIES FROM FINANCING ACTIVITIES (Cont’d)\nNote: (1) The cash flows comprise the net amount of proceeds from borrowings and repayments of borrowings, net interest paid on borrowings, and settlement of swaps for bonds repaid in the statement of cash flows.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "b0b1580d6fd9fc014e30a5f20a25df66", "source_table_id": "9acc7faf491d7a0ed461977c9e5cbcbb"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:acab54535a8bffda62cd53a6216c44ac", "source": "tatqa", "table_key": "447aee3ab90b001550df59cdf0dfd542a7d07597", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 3, "section_rows": [], "cells": [["", "As of", "As of"], ["", "December 31, 2019", "December 31, 2018"], ["Deferred tax assets:", "", ""], ["Reserves and accruals", "$62", "$45"], ["Research and development credits and other credits", "1,730", "1,635"], ["Net operating loss carry forward", "27,907", "25,733"], ["Stock based compensation", "8,402", "8,857"], ["Other", "11", "26"], ["Total deferred tax assets", "38,112", "36,296"], ["Valuation allowance", "(38,112)", "(36,296)"], ["Deferred tax assets after valuation allowance", "—", "—"], ["Total deferred tax liability", "—", "—"], ["Net deferred tax assets (liabilities)", "$—", "$—"]]}, "native_split": "test", "question": "How many components does the deferred tax assets consist of?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "5", "answer_type": "count", "answer_from": "table", "paragraphs": "In 2019 and 2018, we had pre-tax losses of $19,573 and $25,403 respectively, which are available for carry forward to offset future taxable income. We made determinations to provide full valuation allowances for our net deferred tax assets at the end of 2019 and 2018, including NOL carryforwards generated during the years, based on our evaluation of positive and negative evidence, including our history of operating losses and the uncertainty of generating future taxable income that would enable us to realize our deferred tax assets.\nDeferred tax assets (liabilities) consist of the following:\nIn assessing the realization of deferred tax assets, management considers whether it is more likely than not that a portion of the net deferred assets will not be realized. The ultimate realization of the net deferred tax assets is dependent upon the generation of future taxable income during the periods in which those temporary differences become deductible. Based on the available objective evidence, management believes it is more likely than not that the net deferred tax assets at December 31, 2019 will not be realizable. Accordingly, management has maintained a full valuation allowance against its net deferred tax assets at December 31, 2019. The net change in the total valuation allowance for the 12 months ended December 31, 2019 was an increase of $1,816.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "acab54535a8bffda62cd53a6216c44ac", "source_table_id": "8cd44786a6426d17faa6f76b0d645467"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:3699a34071087425aeb759e2a26a4f23", "source": "tatqa", "table_key": "639abf0dbcb6de2622c225e35ec8e3ac84c9e8a1", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "Year ended December 31, 2019", "Year ended December 31, 2018", "Year ended December 31, 2017"], ["North America", "$2,259", "$4,248", "$5,513"], ["Europe", "179,009", "142,688", "124,857"], ["Asia", "67,468", "135,614", "91,552"], ["South America", "232,394", "208,751", "212,616"], ["Other", "1,319", "26,438", "28,511"], ["Total", "$482,449", "$517,739", "$463,049"]]}, "native_split": "test", "question": "How many years did total revenue exceed $500,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nRevenue by Geographic Region\nVessels operate on a worldwide basis and are not restricted to specific locations. Accordingly, it is not possible to allocate the assets of these operations to specific countries. The total net book value of long-lived assets for dry bulk vessels amounted to $741,347 and $933,784 at December 31, 2019 and 2018, respectively. For the Logistics Business, all long-lived assets are located in South America. The total net book value of long-lived assets for the Logistics Business amounted to $536,342 (including constructions in progress of $4,046, referred to in Note 7) and $556,713 at December 31, 2019 and 2018, respectively. The total net book value of long-lived assets for the Containers Business amounted to at December 31, 2018 $399,979.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "3699a34071087425aeb759e2a26a4f23", "source_table_id": "db004e01745b46797e6073aa68868331"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:0ff4afd650a5c72654aaa7c456cc7acb", "source": "tatqa", "table_key": "5d8b74fcb0bf6c244cadf5d28b7e1b9a8733ea06", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended", ""], ["", "April 26, 2019", "April 27, 2018", "April 28, 2017"], ["Shares issued under the ESPP", "3", "4", "4"], ["Proceeds from issuance of shares", "$ 96", "$ 85", "$ 80"]]}, "native_split": "test", "question": "How many years did Shares issued under the ESPP exceed $2 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Employee Stock Purchase Plan\nEligible employees are offered shares through a 24-month offering period, which consists of four consecutive 6-month purchase periods. Employees may purchase a limited number of shares of the Company’s stock at a discount of up to 15% of the lesser of the market value at the beginning of the offering period or the end of each 6-month purchase period. On September 13, 2018, the ESPP was amended to increase the shares reserved for issuance by 2 million shares of common stock. As of April 26, 2019, 7 million shares were available for issuance. The following table summarizes activity related to the purchase rights issued under the ESPP (in millions):", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "0ff4afd650a5c72654aaa7c456cc7acb", "source_table_id": "0ee5b7131ba575a722b6142108145dfd"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:e5c85608d9264333ebf3d056e0f281b5", "source": "tatqa", "table_key": "60be27ee20afd58210826b692931890814193889", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31, 2018"], ["Payroll", "$6,701", "$15,264"], ["Accrued interest", "35,926", "40,903"], ["Accrued voyage expenses", "—", "3,643"], ["Accrued running costs", "—", "42,212"], ["Provision for estimated losses on vessels under time charter", "—", "1,604"], ["Audit fees and related services", "234", "292"], ["Accrued taxes", "8,002", "6,268"], ["Professional fees", "317", "1,251"], ["Other accrued expenses", "—", "12,215"], ["Total accrued expenses", "$51,180", "$123,652"]]}, "native_split": "test", "question": "How many years did professional fees exceed $1,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nNOTE 10: ACCRUED EXPENSES AND OTHER LIABILITIES\nAccrued expenses and other liabilities as of December 31, 2019 and 2018 consisted of the following:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "e5c85608d9264333ebf3d056e0f281b5", "source_table_id": "1c66f3ce250aa12c64b054c72423134d"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:b037c1fcfbad989e3e27543ecc476d1d", "source": "tatqa", "table_key": "3718645a50b055f65348765d5c6d8571f489e7d3", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended August 31,", ""], ["", "2019", "2018", "2017"], ["Intrinsic value of SARS exercised", "$335", "$909", "$5,053"], ["Fair value of restricted stock units vested", "$49,725", "$62,592", "$44,010"], ["Tax benefit for stock compensation expense(1)", "$611", "$1,122", "$560"], ["Unrecognized stock-based compensation expense — restricted stock units", "$41,778", "", ""], ["Remaining weighted-average period for restricted stock units expense", "1.3years", "", ""]]}, "native_split": "test", "question": "How many years did the Tax benefit for stock compensation expense exceed $1,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "The following table represents the restricted stock units and SARS stock-based compensation information for the periods indicated (in thousands):\n(1) Classified as income tax expense within the Consolidated Statements of Operations.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "b037c1fcfbad989e3e27543ecc476d1d", "source_table_id": "4cbc46fc4b5a4a86cbe15ef28007d948"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:25b29d595ff14cb9ff1db4233c8ecfef", "source": "tatqa", "table_key": "bcfa714d49d49f189147cca129478ac54f27b610", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Balance at beginning of the year", "32,750,727", "32,938,466"], ["Repurchases", "(420,770)", "(342,100)"], ["Restricted stock unit issuances", "142,449", "154,361"], ["Balance at end of period", "32,472,406", "32,750,727"]]}, "native_split": "test", "question": "How many years did the Balance at end of period exceed 30,000,000 ?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nNOTE 15 — Shareholders' Equity\nA roll forward of common shares outstanding is as follows:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "25b29d595ff14cb9ff1db4233c8ecfef", "source_table_id": "ee44ebe80ff15b86e094557050dc6306"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:90736d903e8144f60da6b938dce0b6d6", "source": "tatqa", "table_key": "0ec5eda81bca8d2150f792cea1b847c6d3ab5cc2", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["", "", "Years Ended", "", "2019 vs. 2018", ""], ["Years Ended", "July 27, 2019", "July 28, 2018", "July 29, 2017", "Variance in Dollars", "Variance in Percent"], ["Service revenue:", "", "", "", "", ""], ["Americas", "$ 8,173", "$ 7,982", "$7,864", "$191", "2%"], ["Percentage of service revenue", "63.4%", "63.3%", "63.9%", "", ""], ["EMEA", "2,854", "2,754", "2,635", "100", "4%"], ["Percentage of service revenue", "22.1%", "21.8%", "21.4%", "", ""], ["APJC", "1,872", "1,885", "1,801", "(13)", "(1)%"], ["Percentage of service revenue", "14.5%", "14.9%", "14.7%", "", ""], ["Total", "$ 12,899", "$ 12,621", "$12,300", "$278", "2%"]]}, "native_split": "test", "question": "How many years did service revenue from APJC exceed $1,500 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Service Revenue by Segment\nThe following table presents the breakdown of service revenue by segment (in millions, except percentages):\nAmounts may not sum and percentages may not recalculate due to rounding\nService revenue increased 2%, driven by an increase in software and solution support offerings. Service revenue increased in the Americas and EMEA segments, partially offset by decreased revenue in our APJC segment.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "90736d903e8144f60da6b938dce0b6d6", "source_table_id": "4b4d7f4fdc70ff44fecb6b6844cdb1bd"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:b14936742439bf08e0408b9fb10b922e", "source": "tatqa", "table_key": "c4d56953f8bd22a6ac5885505c6246ca777520a5", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Projected benefit obligation", "$3,778", "$3,848"], ["Accumulated benefit obligation", "$2,999", "$3,028"], ["Fair value of plan assets", "$1,418", "$1,426"]]}, "native_split": "test", "question": "How many years did the Accumulated benefit obligation exceed $3,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nThe projected benefit obligation, accumulated benefit obligation and fair value of plan assets for those Pension Plans with accumulated benefit obligation in excess of fair value of plan assets is shown below:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "b14936742439bf08e0408b9fb10b922e", "source_table_id": "dc0d8f2313478f7e229f7f76985d90ee"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:a2aee8fbc00c2c220a8cb7e5214fc17e", "source": "tatqa", "table_key": "98a9c2937e3c5f3d75af76ffcf5982fa5181ee64", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "March 31,", ""], ["", "2019", "2018"], ["Accrued compensation and benefits", "$133.2", "$87.6"], ["Income taxes payable", "46.9", "27.5"], ["Sales related reserves", "366.9", "—"], ["Accrued expenses and other liabilities", "240.3", "114.5"], ["Total accrued liabilities", "$787.3", "$229.6"]]}, "native_split": "test", "question": "How many years did Accrued expenses and other liabilities exceed $200 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Accrued Liabilities\nAccrued liabilities consists of the following (in millions):\nSales related reserves represent price concessions and stock rotation rights that the Company offers to many of its distributors. For the fiscal year ending March 31, 2018, these sales related reserves were recorded within accounts receivable, and therefore did not exist within accrued liabilities. The Company made this change in classification as part of its adoption of ASC 606. For additional information regarding the Company's adoption of ASC 606, refer to Note 1 of the consolidated financial statements.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "a2aee8fbc00c2c220a8cb7e5214fc17e", "source_table_id": "a06963067573666d15cc4484808dd226"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:f7ca7ef1453d0be5a5b9041287650727", "source": "tatqa", "table_key": "2eaa3c42273776b1475ec070ae78568610a09117", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["", "", "", "Year Ended December 31,", "", ""], ["", "2019", "2018", "2017", "2016", "2015"], ["Adjusted EBITDA:", "", "", "", "", ""], ["Net income", "$53,330", "$21,524", "$29,251", "$10,154", "$11,768"], ["Adjustments:", "", "", "", "", ""], ["Interest expense, interest income and other income, net", "(8,483)", "503", "1,133", "(323)", "526"], ["Provision for / (benefit from) income taxes", "5,566", "(9,825)", "2,990", "4,227", "5,697"], ["Amortization and depreciation expense", "22,134", "21,721", "17,734", "6,490", "5,808"], ["Stock-based compensation expense", "20,603", "13,429", "7,413", "4,001", "4,124"], ["Acquisition-related expense", "2,403", "—", "5,895", "11,098", "100"], ["Litigation expense", "12,754", "45,729", "7,212", "13,387", "6,347"], ["Total adjustments", "54,977", "71,557", "42,377", "38,880", "22,602"], ["Adjusted EBITDA", "$108,307", "$93,081", "$71,628", "$49,034", "$34,370"]]}, "native_split": "test", "question": "How many years did the total adjustments exceed $50,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table", "paragraphs": "We have included Adjusted EBITDA in this report because it is a key measure that our management uses to understand and evaluate our core operating performance and trends, to generate future operating plans, to make strategic decisions regarding the allocation of capital and to make investments in initiatives that are focused on cultivating new markets for our solutions. We also use certain non-GAAP financial measures, including Adjusted EBITDA, as performance measures under our executive bonus plan. Further, we believe the exclusion of certain expenses in calculating Adjusted EBITDA facilitates comparisons of our operating performance on a period-to-period basis and, in the case of exclusion of acquisition-related expense and certain historical legal expenses, excludes items that we do not consider to be indicative of our core operating performance. Accordingly, we believe that Adjusted EBITDA provides useful information to investors and others in understanding and evaluating our operating results in the same manner as our management and board of directors.\nOur use of Adjusted EBITDA has limitations as an analytical tool, and you should not consider it in isolation or as a substitute for analysis of our financial results as reported under GAAP. Some of these limitations are: (a) although depreciation and amortization are non-cash charges, the assets being depreciated and amortized may have to be replaced in the future, and Adjusted EBITDA does not reflect cash capital expenditure requirements for such replacements or for new capital expenditure requirements; (b) Adjusted EBITDA does not reflect changes in, or cash requirements for, our working capital needs; (c) Adjusted EBITDA does  may represent a reduction in cash available to us; and (e) other companies, including companies in our industry, may calculate Adjusted EBITDA or similarly titled measures differently, which reduces its usefulness as a comparative measure.\nBecause of these and other limitations, you should consider Adjusted EBITDA alongside our other GAAP-based financial performance measures, our net income and our other GAAP financial results. The following table presents a reconciliation of Adjusted EBITDA to net income, the most directly comparable GAAP measure, for each of the periods indicated (in thousands).", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "f7ca7ef1453d0be5a5b9041287650727", "source_table_id": "8c6114af79ce7f7775b24ac57a2c7b64"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:7e253c4c03cd41e26f15404228d32ed6", "source": "tatqa", "table_key": "1294c5df8532bc0e12900d666515b096c0b2d30e", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["", "2019", "2018", "2017"], ["Supplemental cash flow information:", "", "", ""], ["Cash paid for interest", "$750.2", "$789.7", "$712.1"], ["Cash paid for income taxes (net of refunds of $11.2, $25.0 and $20.7, respectively)", "147.5", "163.9", "136.5"], ["Non-cash investing and financing activities:", "", "", ""], ["(Decrease) increase in accounts payable and accrued expenses for purchases of property and equipment and construction activities", "(21.0)", "8.3", "34.0"], ["Purchases of property and equipment under finance leases, perpetual easements and capital leases", "81.3", "57.8", "54.8"], ["Fair value of debt assumed through acquisitions", "329.8", "—", "—"], ["Acquisition of Commercialization Rights (1)", "—", "24.8", "—"], ["Conversion of third-party debt to equity", "—", "—", "48.2"], ["Debt financed acquisition of communication sites", "—", "54.2", "—"]]}, "native_split": "test", "question": "How many years did the cash paid for income taxes exceed $150 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "AMERICAN TOWER CORPORATION AND SUBSIDIARIES NOTES TO CONSOLIDATED FINANCIAL STATEMENTS\n20. SUPPLEMENTAL CASH FLOW INFORMATION\nSupplemental cash flow information and non-cash investing and financing activities are as follows for the years ended December 31,:\n(1) Related to the note extinguishment with TV Azteca, S.A. de C.V. in 2018.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "7e253c4c03cd41e26f15404228d32ed6", "source_table_id": "dd72572fa20be30bb762c6d9646ca39d"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:7cfbd3635aa4d685ea4950b5fc4279c7", "source": "tatqa", "table_key": "fffa2171b9fca36b44debaa052dce0bad8b45f71", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Operating income (loss):", "", ""], ["AMER ", "$57.8", "$38.6"], ["APAC ", "208.2", "213.9"], ["EMEA ", "4.5", "1.5"], ["Corporate and other costs ", "(128.4)", "(135.7)"], ["Total operating income", "$142.1", "$118.3"]]}, "native_split": "test", "question": "How many years did the operating income from EMEA exceed $3 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "A discussion of operating income by reportable segment is presented below (in millions):\nAMER. Operating income increased $19.2 million in fiscal 2019 as compared to fiscal 2018, primarily as a result of the increase in net sales and a positive shift in customer mix, partially offset by increased fixed costs to support new program ramps.\nAPAC. Operating income decreased $5.7 million in fiscal 2019 as compared to fiscal 2018, primarily as a result of a negative shift in customer mix and increased fixed costs to support new program ramps, partially offset by the increase in net sales.\nEMEA. Operating income increased $3.0 million in fiscal 2019 as compared to fiscal 2018 primarily as a result of the increase in net sales and a positive shift in customer mix, partially offset by increased fixed costs to support new program ramps.\nOther expense. Other expense for fiscal 2019 increased $5.4 million as compared to fiscal 2018. The increase in other expense for fiscal 2019 was primarily due to a $2.7 million decrease in interest income as a result of lower cash balances and a $1.5 million increase in factoring fees related to the Company's accounts receivable sale programs.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 5, "source_local_id": "7cfbd3635aa4d685ea4950b5fc4279c7", "source_table_id": "3221ab99f4eec6b8d95137c48051aa5a"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:bfa59cdb9860b60f23097cf93a0d53f9", "source": "tatqa", "table_key": "ddc4dc0073d93100d802d9b63d6d0815c77a7f38", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "July 27, 2019", "July 28, 2018", "Increase (Decrease)"], ["Lease receivables, net", "$2,326", "$2,576", "$(250)"], ["Loan receivables, net .", "5,367", "4,939", "428"], ["Financed service contracts, net", "2,360", "2,316", "44"], ["Total, net", "$10,053", "$9,831", "$222"]]}, "native_split": "test", "question": "How many years did net financed service contracts exceed $2,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "Financing Receivables and Guarantees The following table summarizes our financing receivables (in millions):\nFinancing Receivables Our financing arrangements include leases, loans, and financed service contracts. Lease receivables include sales-type and direct-financing leases. Arrangements related to leases are generally collateralized by a security interest in the underlying assets. Our loan receivables include customer financing for purchases of our hardware, software and services and also may include additional funds for other costs associated with network installation and integration of our products and services. We also provide financing to certain qualified customers for long-term service contracts, which primarily relate to technical support services. The majority of the revenue from these financed service contracts is deferred and is recognized ratably over the period during which the services are performed. Financing receivables increased by 2%. We expect to continue to expand the use of our financing programs in the near term.\nFinancing Guarantees In the normal course of business, third parties may provide financing arrangements to our customers and channel partners under financing programs. The financing arrangements to customers provided by third parties are related to leases and loans and typically have terms of up to three years. In some cases, we provide guarantees to third parties for these lease and loan arrangements. The financing arrangements to channel partners consist of revolving short-term financing provided by third parties, generally with payment terms ranging from 60 to 90 days. In certain instances, these financing arrangements result in a transfer of our receivables to the third party. The receivables are derecognized upon transfer, as these transfers qualify as true sales, and we receive payments for the receivables from the third party based on our standard payment terms.\nThe volume of channel partner financing was $29.6 billion, $28.2 billion, and $27.0 billion in fiscal 2019, 2018, and 2017, respectively. These financing arrangements facilitate the working capital requirements of the channel partners, and in some cases, we guarantee a portion of these arrangements. The balance of the channel partner financing subject to guarantees was $1.4 billion and $1.0 billion as of July 27, 2019 and July 28, 2018, respectively. We could be called upon to make payments under these guarantees in the event of nonpayment by the channel partners or end-user customers. Historically, our payments under these arrangements have been immaterial. Where we provide a guarantee, we defer the revenue associated with the channel partner and end-user financing arrangement in accordance with revenue recognition policies, or we record a liability for the fair value of the guarantees. In either case, the deferred revenue is recognized as revenue when the guarantee is removed. As of July 27, 2019, the total maximum potential future payments related to these guarantees was approximately $218 million, of which approximately $77 million was recorded as deferred revenue.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "bfa59cdb9860b60f23097cf93a0d53f9", "source_table_id": "5e06ef4f2fa578664ca61a5d2a5f676e"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:b4eea9ff6cbab8ca09b4e3ad98c064e1", "source": "tatqa", "table_key": "498ff9cb6c92ee50f5da35a6f3660e4ee7e829e5", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "Fiscal 2019", "Fiscal 2018", "% Change"], ["", "", "(in millions)", ""], ["Sales", "$  849.8", "$  670.7", "27 %"], ["Operating income", "77.2", "60.4", "28"], ["Adjusted EBITDA", "110.5", "73.3", "51"]]}, "native_split": "test", "question": "How many items had a % change that is less than 50%?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table", "paragraphs": "SEGMENT RESULTS\nCubic Transportation Systems\nSales: CTS sales increased 27% to $849.8 million in 2019 compared to $670.7 million in 2018, including the impact of the adoption of ASC 606. The increase in sales was primarily driven by growth in both organic and inorganic business in North America. Sales in 2019 were higher in the U.S. primarily due to system development on contracts in New York, Boston, and the San Francisco Bay Area. Businesses acquired by CTS during fiscal year 2019, whose operations are all located in the U.S., had sales of $74.4 million in fiscal year 2019. Sales increased slightly in Australia between fiscal years 2018 and 2019 as increased system development work on a contract in Brisbane was partially offset by the negative impact of foreign currency exchange rates as well as a decrease in service sales. Sales were lower in the UK primarily due to a decrease in system development work in London and the negative impact of currency exchange rates. The average exchange rates between the prevailing currencies in our foreign operations and the U.S. dollar resulted in a decrease in CTS sales of $22.2 million for 2019 compared to 2018, primarily due to the strengthening of the U.S. dollar against the British pound and Australian dollar.\nAmortization of Purchased Intangibles: Amortization of purchased intangibles included in the CTS operating results totaled $22.0 million in 2019 and $5.2 million in 2018. The increase is due to the amortization of purchased intangibles for companies acquired by CTS in fiscal year 2019.\nOperating Income: CTS operating income increased 28% in 2019 to $77.2 million compared to $60.4 million in 2018. The increase in operating income was primarily caused by higher margins on increased work on development projects in New York, Boston, the San Francisco Bay Area and Brisbane, as well as the impact of the adoption of ASC 606. These increases in operating income were partially offset by operating losses incurred by businesses acquired by CTS in fiscal 2019 as well as the negative impact of changes in foreign currency exchange rates. Businesses acquired by CTS in fiscal years 2019 incurred operating losses of $10.1 million in fiscal 2019, which included acquisition transaction costs of $8.1 million and amortization of intangible assets totaling $19.3 million. The average exchange rates between the prevailing currency in our foreign operations and the U.S. dollar resulted in a decrease in CTS operating income of $3.6 million for 2019 compared to 2018.\nAdjusted EBITDA: CTS Adjusted EBITDA increased 51% to $110.5 million in 2019 compared to $73.3 million in 2018. The increase in Adjusted EBITDA was primarily driven by the same factors that drove the increase in operating income described above excluding the increases in amortization of purchased intangibles and acquisition transaction costs which are excluded from Adjusted EBITDA. Adjusted EBITDA for CTS increased by $2.3 million in 2019 as a result of the adoption of the new revenue recognition standard.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 6, "source_local_id": "b4eea9ff6cbab8ca09b4e3ad98c064e1", "source_table_id": "0fa66bdde5dce465760cf558173d40c6"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:55f98f213e0bf08cb4638a6a11395b1f", "source": "tatqa", "table_key": "65d9b8685d4d923b759b0e9cd26e47c785b98c67", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["", "2015", "2016", "2017", "20181", "2019"], ["Net Income", "$672", "$970", "$1,225", "$1,265", "$1,917"], ["Income tax provision (benefit)", "158", "156", "31", "(110)", "(0)"], ["Other expense (income)", "135", "48", "(31)", "(24)", "(18)"], ["Loss (gain) on retirement of long-term obligations", "80", "(1)", "70", "3", "22"], ["Interest expense", "596", "717", "750", "826", "814"], ["Interest income", "(17)", "(26)", "(35)", "(55)", "(47)"], ["Other operating expenses", "67", "73", "256", "513", "166"], ["Depreciation, amortization and accretion", "1,285", "1,526", "1,716", "2,111", "1,778"], ["Stock-based compensation expense", "91", "90", "109", "138", "111"], ["ADJUSTED EBITDA", "$3,067", "$3,553", "$4,090", "$4,667", "$4,745"], ["Divided by total revenue", "$4,772", "$5,786", "$6,664", "$7,440", "$7,580"], ["ADJUSTED EBITDA MARGIN", "64%", "61%", "61%", "63%", "63%"]]}, "native_split": "test", "question": "How many years did adjusted EBITDA exceed $4,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table-text", "paragraphs": "American Tower Corporation • 2019 Annual Report\nAppendix 1 • Letter to Stakeholders\nRECONCILIATION OF ADJUSTED EBITDA TO NET INCOME ($ in millions. Totals may not add due to rounding.)", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "55f98f213e0bf08cb4638a6a11395b1f", "source_table_id": "094094dd16bd0f5d4ff18b46b17e53fb"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:225ea4dcde7205d639519cc22c3e2a53", "source": "tatqa", "table_key": "240822e184d8a710d3bdb392e230070256ea534c", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["Company - 2018", "Provisions", "Others", "Total"], ["Deferred tax assets", "S$ Mil", "S$ Mil", "S$ Mil"], ["Balance as at 1 April 2017", "0.3", "2.8", "3.1"], ["Effects of adoption of SFRS(I) 15", "-", "(1.0)", "(1.0)"], ["Balance as at 1 April 2017, restated", "0.3", "1.8", "2.1"], ["Credited to income statement", "0.2", "9.0", "9.2"], ["Balance as at 31 March 2018", "0.5", "10.8", "11.3"], ["", "", "Accelerated tax", ""], ["Company - 2018", "", "depreciation", "Total"], ["Deferred tax liabilities", "", "S$ Mil", "S$ Mil"], ["Balance as at 1 April 2017", "", "(285.3)", "(285.3)"], ["Effects of adoption of SFRS(I) 1", "", "10.2", "10.2"]]}, "native_split": "test", "question": "How many factors were there that gave rise to Deferred tax assets in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table", "paragraphs": "Notes: (1) TWDV – Tax written down value\n(2) NBV – Net book value\nDeferred tax assets and liabilities are offset when there is a legally enforceable right to set-off current tax assets against current tax liabilities, and when deferred income taxes relate to the same fiscal authority.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "225ea4dcde7205d639519cc22c3e2a53", "source_table_id": "3c70a27ee159768e51821887c914a814"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:ef919c08ed12587a2554c92cc4889944", "source": "tatqa", "table_key": "71cf29a82b17ef72e5c76aa585beebd2fd210b38", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "September 28,\n2019", "September 29,\n2018"], ["Total assets:", "", ""], ["AMER ", "$751,990", "$645,791"], ["APAC ", "958,744", "937,510"], ["EMEA ", "209,541", "193,797"], ["Corporate and eliminations", "80,608", "155,544"], ["", "$2,000,883", "$1,932,642"]]}, "native_split": "test", "question": "How many years did total assets exceed $2,000,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "11. Reportable Segments, Geographic Information and Major Customers\nReportable segments are defined as components of an enterprise about which separate financial information is available that is evaluated regularly by the chief operating decision maker, or group, in assessing performance and allocating resources. The Company uses an internal management reporting system, which provides important financial data to evaluate performance and allocate the Company’s resources on a regional basis. Net sales for the segments are attributed to the region in which the product is manufactured or the service is performed. The services provided, manufacturing processes used, class of customers serviced and order fulfillment processes used are similar and generally interchangeable across the segments. A segment’s performance is evaluated based upon its operating income (loss). A segment’s operating income (loss) includes its net sales less cost of sales and selling and administrative expenses, but excludes corporate and other expenses. Corporate and other expenses  fiscal 2019 and the $13.5 million one-time employee bonus paid to full-time, non-executive employees during fiscal 2018 due to the Company's ability to access overseas cash as a result of Tax Reform (the \"one-time employee bonus\"). These costs are not allocated to the segments, as management excludes such costs when assessing the performance of the segments. Inter-segment transactions are generally recorded at amounts that approximate arm’s length transactions. The accounting policies for the segments are the same as for the Company taken as a whole.\nInformation about the Company’s three reportable segments for fiscal 2019, 2018 and 2017 is as follows (in thousands):", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "ef919c08ed12587a2554c92cc4889944", "source_table_id": "637fab7088ea6c78a5dba55f17e833bd"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:5166422113d04fdfaebf33cfb9a9e402", "source": "tatqa", "table_key": "489541dd3f7cc884627219b7e6f852ea934dbe20", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "", "", "INTERNAL CREDIT RISK RATING", ""], ["July 27, 2019", "1 to 4", "5 to 6", "7 and Higher", "Total"], ["Lease receivables", "$1,204", "$991", "$35", "$2,230"], ["Loan receivables", "3,367", "1,920", "151", "5,438"], ["Financed service contracts", "1,413", "939", "17", "2,369"], ["Total", "$5,984", "$3,850", "$203", "$10,037"]]}, "native_split": "test", "question": "How many gross receivables had a total that exceeded $4,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table-text", "paragraphs": "(b) Credit Quality of Financing Receivables\nGross receivables, excluding residual value, less unearned income categorized by our internal credit risk rating as of July 27, 2019 and July 28, 2018 are summarized as follows (in millions):\nWe determine the adequacy of our allowance for credit loss by assessing the risks and losses inherent in our financing receivables by portfolio segment. The portfolio segment is based on the types of financing offered by us to our customers, which consist of the following: lease receivables, loan receivables, and financed service contracts.\nOur internal credit risk ratings of 1 through 4 correspond to investment-grade ratings, while credit risk ratings of 5 and 6 correspond to non-investment grade ratings. Credit risk ratings of 7 and higher correspond to substandard ratings.", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 4, "source_local_id": "5166422113d04fdfaebf33cfb9a9e402", "source_table_id": "7f4f100eb8ea1c0d35ea646699df8978"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:defc0cd33399804bbb0cf71cd9a7f187", "source": "tatqa", "table_key": "92b21e2a6aaa43a35bcc0fbdff7138ed113f1bbf", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Land and land improvements", "$1,095", "$1,136"], ["Buildings and improvements", "68,350", "70,522"], ["Machinery and equipment", "224,312", "231,619"], ["Less: Accumulated depreciation", "(188,719)", "(203,876)"], ["Property, plant and equipment, net", "$105,038", "$99,401"]]}, "native_split": "test", "question": "How many years did the amount of Machinery and equipment exceed $200,000 thousand?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nNOTE 5 — Property, Plant and Equipment\nProperty, plant and equipment is comprised of the following:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "defc0cd33399804bbb0cf71cd9a7f187", "source_table_id": "903322d78b47417847ca16d46feb6653"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:a97fbfe924092a6c5a4986926b369287", "source": "tatqa", "table_key": "462d2fa1f8dbaba21287d47e2fc622f8a3f0770b", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "Unrealized Holding Gains (Losses) Available-for-sale Securities", "Minimum Pension Liability", "Foreign Currency", "Total"], ["Balance at March 31, 2017", "$0.3", "$(5.3)", "$(9.4)", "$(14.4)"], ["Other comprehensive loss before reclassifications", "(13.6)", "(5.6)", "—", "(19.2)"], ["Amounts reclassified from accumulated other comprehensive income (loss)", "15.2", "0.8", "—", "16.0"], ["Net other comprehensive loss", "1.6", "(4.8)", "—", "(3.2)"], ["Balance at March 31, 2018", "$1.9", "$(10.1)", "$(9.4)", "$(17.6)"]]}, "native_split": "test", "question": "How many years did the balance Unrealized Holding Gains Available-for-sale Securities exceed $1 million? ", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "1", "answer_type": "count", "answer_from": "table", "paragraphs": "Accumulated Other Comprehensive Income\nThe following tables present the changes in the components of accumulated other comprehensive income (AOCI) for the years ended March 31, 2019 and 2018:", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "a97fbfe924092a6c5a4986926b369287", "source_table_id": "c1cc798b575a99c9d13a83e21df88ff9"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:94da20b77fb237de1d6f07f8b457987d", "source": "tatqa", "table_key": "d1e64b27843c7d66dca6411eab8a1a4aa56b2727", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended", ""], ["", "April 26, 2019", "April 27, 2018", "April 28, 2017"], ["Current:", "", "", ""], ["Federal", "$ 26", "$ 764", "$ 22"], ["State", "27", "10", "3"], ["Foreign", "49", "39", "41"], ["Total current", "102", "813", "66"], ["Deferred:", "", "", ""], ["Federal", "35", "239", "61"], ["State", "(6 )", "27", "17"], ["Foreign", "(32 )", "4", "(4 )"], ["Total deferred", "(3 )", "270", "74"], ["Provision for income taxes", "$ 99", "$ 1,083", "$ 140"]]}, "native_split": "test", "question": "How many years did current foreign provision for income tax exceed $40 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2", "answer_type": "count", "answer_from": "table-text", "paragraphs": "14. Income Taxes\nThe provision for income taxes consists of the following (in millions):", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 2, "source_local_id": "94da20b77fb237de1d6f07f8b457987d", "source_table_id": "4db053bfdeacfc66ec24c351511a110a"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:fabe3b6a76158d5e91d4d3bab10fe51e", "source": "tatqa", "table_key": "65d9b8685d4d923b759b0e9cd26e47c785b98c67", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["", "2015", "2016", "2017", "20181", "2019"], ["Net Income", "$672", "$970", "$1,225", "$1,265", "$1,917"], ["Income tax provision (benefit)", "158", "156", "31", "(110)", "(0)"], ["Other expense (income)", "135", "48", "(31)", "(24)", "(18)"], ["Loss (gain) on retirement of long-term obligations", "80", "(1)", "70", "3", "22"], ["Interest expense", "596", "717", "750", "826", "814"], ["Interest income", "(17)", "(26)", "(35)", "(55)", "(47)"], ["Other operating expenses", "67", "73", "256", "513", "166"], ["Depreciation, amortization and accretion", "1,285", "1,526", "1,716", "2,111", "1,778"], ["Stock-based compensation expense", "91", "90", "109", "138", "111"], ["ADJUSTED EBITDA", "$3,067", "$3,553", "$4,090", "$4,667", "$4,745"], ["Divided by total revenue", "$4,772", "$5,786", "$6,664", "$7,440", "$7,580"], ["ADJUSTED EBITDA MARGIN", "64%", "61%", "61%", "63%", "63%"]]}, "native_split": "test", "question": "How many years did net income exceed $1,000 million?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3", "answer_type": "count", "answer_from": "table-text", "paragraphs": "American Tower Corporation • 2019 Annual Report\nAppendix 1 • Letter to Stakeholders\nRECONCILIATION OF ADJUSTED EBITDA TO NET INCOME ($ in millions. Totals may not add due to rounding.)", "scale": "", "scoring": "tolerance", "meta": {"paragraph_count": 3, "source_local_id": "fabe3b6a76158d5e91d4d3bab10fe51e", "source_table_id": "094094dd16bd0f5d4ff18b46b17e53fb"}, "dataset": "tatqa", "subtype": "count"}
{"id": "tatqa:cb505f309e704f569dd290ed57bf1ee1", "source": "tatqa", "table_key": "7a878c4eb3435f958999b08c4b4decb993c018d2", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Depreciation expense", "$2,540", "$2,320", "$2,149"], ["Amortization expense", "4,764", "2,158", "1,834"], ["Impairment losses", "—", "19", "78"]]}, "native_split": "test", "question": "Which years does the table show?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018", "2017"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "GreenSky, Inc. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS — (Continued) (United States Dollars in thousands, except per share data, unless otherwise stated)\nNote 6. Property, Equipment and Software\nThe following table shows depreciation and amortization expense, as well as recorded impairment losses related to abandoned capitalized software projects that are recorded within general and administrative expense in the Consolidated Statements of Operations. We determined that these software projects would not generate future cash flows through use or disposal to a third party and, as such, the fair value as of the respective reporting dates was $0.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "cb505f309e704f569dd290ed57bf1ee1", "source_table_id": "414d3b3dabd0898d3de089e2cc2d6bf5"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:fe761f34054c808d50ab6a40f5c7f341", "source": "tatqa", "table_key": "639abf0dbcb6de2622c225e35ec8e3ac84c9e8a1", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "Year ended December 31, 2019", "Year ended December 31, 2018", "Year ended December 31, 2017"], ["North America", "$2,259", "$4,248", "$5,513"], ["Europe", "179,009", "142,688", "124,857"], ["Asia", "67,468", "135,614", "91,552"], ["South America", "232,394", "208,751", "212,616"], ["Other", "1,319", "26,438", "28,511"], ["Total", "$482,449", "$517,739", "$463,049"]]}, "native_split": "test", "question": "Which years does the table provide information for revenue by geographic region?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018", "2017"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nRevenue by Geographic Region\nVessels operate on a worldwide basis and are not restricted to specific locations. Accordingly, it is not possible to allocate the assets of these operations to specific countries. The total net book value of long-lived assets for dry bulk vessels amounted to $741,347 and $933,784 at December 31, 2019 and 2018, respectively. For the Logistics Business, all long-lived assets are located in South America. The total net book value of long-lived assets for the Logistics Business amounted to $536,342 (including constructions in progress of $4,046, referred to in Note 7) and $556,713 at December 31, 2019 and 2018, respectively. The total net book value of long-lived assets for the Containers Business amounted to at December 31, 2018 $399,979.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "fe761f34054c808d50ab6a40f5c7f341", "source_table_id": "db004e01745b46797e6073aa68868331"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:100171f6bba38d5bbaa9a8cc03ea98f9", "source": "tatqa", "table_key": "b29a7c68ff90ce61614f29785dc5c5890f008fad", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "", "December 31,", "", ""], ["(in thousands of $)", "2019", "2018", "Change", "% Change"], ["Share in net (loss)/earnings in Golar Partners", "(20,050)", "7,001", "(27,051)", "(386)%"], ["Impairment of investment in Golar Partners", "—", "(149,389)", "149,389", "100%"], ["Share of net (losses)/earnings in other affiliates", "(2,515)", "3,711", "(6,226)", "(168)%"], ["", "(22,565)", "(138,677)", "116,112", "(84)%"]]}, "native_split": "test", "question": "In which years was the equity in net earnings of affiliates recorded for?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Equity in net earnings of affiliates:\nAs of December 31, 2019, we held a 32.0% (2018: 32.0%) ownership interest in Golar Partners (including our 2% general partner interest) and 100% of the incentive distribution rights (\"IDRs\"). The decrease in the share of net earnings in Golar Partners is due to a decrease in underlying performance of Golar Partners and fair value adjustment for the year ended December 31, 2019. The decrease in the share of net earnings in Golar Partners is offset by the movement of the impairment charge of $149.4 million recognized for the year ended December 31, 2018.\nThe share of net earnings in other affiliates represents our share of equity in Egyptian Company for Gas Services S.A.E (\"ECGS\") and Avenir LNG Limited (\"Avenir\"). During the year ended December 31, 2018 we recognized negative goodwill of $3.8 million in equity in net earnings of affiliates to reflect our bargain purchase of Avenir. Refer to note 14 \"Investment in Affiliates\" of our consolidated financial statements included herein for further details.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "100171f6bba38d5bbaa9a8cc03ea98f9", "source_table_id": "69784efbba041db5d817cb59bc7305d2"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:b7f62a733fcaec4086dd98ba0594bf4f", "source": "tatqa", "table_key": "88206c422e0fb015c8e740f18e18a8a28804c7f1", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended June 30,", ""], ["", "2019", "2018", "2017"], ["Fiscal 2019 Restructuring Plan", "$28,318", "$—", "$—"], ["Fiscal 2018 Restructuring Plan", "515", "10,154", "—"], ["Fiscal 2017 Restructuring Plan", "898", "7,207", "33,827"], ["Restructuring Plans prior to Fiscal 2017 Restructuring Plan", "(620)", "279", "(340)"], ["Acquisition-related costs", "5,625", "4,805", "15,938"], ["Other charges (recoveries)", "983", "6,766", "14,193"], ["Total", "$35,719", "$29,211", "$63,618"]]}, "native_split": "test", "question": "What Fiscal years are included in the table?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018", "2017"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "NOTE 17—SPECIAL CHARGES (RECOVERIES)\nSpecial charges (recoveries) include costs and recoveries that relate to certain restructuring initiatives that we have undertaken from time to time under our various restructuring plans, as well as acquisition-related costs and other charges.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "b7f62a733fcaec4086dd98ba0594bf4f", "source_table_id": "bb3b12efd430ed81ccc8698c08ea8965"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:68ee1ba162e4656d81d903042bd952db", "source": "tatqa", "table_key": "f991a5bf67940fe66d1306ec5562e59061e96c3e", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["Name", "Fees Earned or Paid in Cash ($)", "Stock Awards ($)", "Option Awards ($)", "Total($)"], ["Richard S. Hill", "$106,000", "$149,987", "—", "$255,987"], ["Darcy Antonellis", "$55,333", "$149,987", "—", "$205,320"], ["David C. Habiger", "$70,000", "$149,987", "—", "$219,987"], ["V. Sue Molina", "$81,000", "$149,987", "—", "$230,987"], ["George A. Riedel", "$68,333", "$149,987", "—", "$218,320"], ["Christopher A. Seams", "$82,000", "$149,987", "—", "$231,987"]]}, "native_split": "test", "question": "How much are the total compensations for Richard S. Hill and Christopher A. Seams, respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["255,987", "231,987"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "A non-employee director who is initially appointed after any annual meeting of stockholders will receive a restricted stock unit award or option grant on the date of his or her initial appointment to the Board of Directors equal to the pro-rated amount of the annual grant\nAnnual option grants and restricted stock unit awards (or any pro-rated grants for directors initially appointed between annual meetings) vest on the earlier to occur of the first anniversary of the date of grant or the next annual meeting of stockholders. No portion of an option automatically granted to a director is exercisable after the tenth anniversary after the date of option grant. Additionally, an option automatically granted to a director may be exercisable after the termination of the director’s services as described in the option agreement, generally ending three months after such termination.\nThe following table shows compensation information for our non-employee directors for fiscal year 2019.\n2019 Director Compensation Table\n(1) The amounts reflected in this column represent the aggregate grant date fair value for stock awards granted to our non-employee directors in 2019, measured in accordance with ASC 718, excluding the effect of estimated forfeitures, and do not reflect whether the recipient has actually realized a financial benefit from these awards. For the methodology of how the aggregate grant date fair value amount is calculated, please see Note 13 of the Notes to Consolidated Financial Statements included in this Form 10-K. The aggregate number of shares subject to unvested restricted stock unit awards outstanding for each non-employee director at December 31, 2019 was: Mr. Hill: 5,889; Ms. Antonellis: 5,889; Mr. Habiger: 5,889; Ms. Molina: 5,889; Mr. Riedel: 5,889 and Mr. Seams: 5,889. None of the non-employee directors held any stock options as of December 31, 2019.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 5, "source_local_id": "68ee1ba162e4656d81d903042bd952db", "source_table_id": "9246b58ea8656eecf7739094d072f0b8"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:79f0826d2abbaf9504093e1cc562613b", "source": "tatqa", "table_key": "49664652cf9c2e11a00b63848044b139c03c04fd", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Years Ended", ""], ["($ in millions)", "May 26, 2019", "May 27, 2018", "May 28, 2017"], ["Net derivative gains (losses) incurred", "$(3.6)", "$(0.9)", "$0.6"], ["Less: Net derivative gains (losses) allocated to reporting segments", "(1.8)", "(7.1)", "5.7"], ["Net derivative gains (losses) recognized in general corporate expenses", "$(1.8)", "$6.2", "$(5.1)"], ["Net derivative gains (losses) allocated to Grocery & Snacks", "$(2.1)", "$0.2", "$3.4"], ["Net derivative gains (losses) allocated to Refrigerated & Frozen", "(1.1)", "(0.3)", "0.8"], ["Net derivative gains (losses) allocated to International Foods .", "2.8", "(6.9)", "1.6"], ["Net derivative losses allocated to Foodservice .", "(0.6)", "(0.1)", "—"], ["Net derivative losses allocated to Pinnacle Foods", "(0.8)", "—", "—"], ["Net derivative losses allocated to Commercial .", "—", "—", "(0.1)"], ["Net derivative gains (losses) included in segment operating profit .", "$(1.8)", "$(7.1)", "$5.7"]]}, "native_split": "test", "question": "What are the expected operating fiscal gains in fiscal 2020 and 2021 and thereafter, respectively, based on the company’s forecasts of the timing of recognition of the underlying hedged items?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$0.9 million", "$0.5 million"], "answer_type": "multi-span", "answer_from": "text", "paragraphs": "Presentation of Derivative Gains (Losses) from Economic Hedges of Forecasted Cash Flows in Segment Results\nDerivatives used to manage commodity price risk and foreign currency risk are not designated for hedge accounting treatment. We believe these derivatives provide economic hedges of certain forecasted transactions. As such, these derivatives are generally recognized at fair market value with realized and unrealized gains and losses recognized in general corporate expenses. The gains and losses are subsequently recognized in the operating results of the reporting segments in the period in which the underlying transaction being economically hedged is included in earnings. In the event that management determines a particular derivative entered into as an economic hedge of a forecasted commodity purchase has ceased to function as an economic hedge, we cease recognizing further gains and losses on such derivatives in corporate expense and begin recognizing such gains and losses within segment operating results, immediately.\nThe following table presents the net derivative gains (losses) from economic hedges of forecasted commodity consumption and the foreign currency risk of certain forecasted transactions associated with continuing operations, under this methodology:\nAs of May 26, 2019, the cumulative amount of net derivative gains from economic hedges that had been recognized in general corporate expenses and not yet allocated to reporting segments was $1.4 million. This amount reflected net gains of $1.0 million incurred during the fiscal year ended May 26, 2019, as well as net gains of $0.4 million incurred prior to fiscal 2019. Based on our forecasts of the timing of recognition of the underlying hedged items, we expect to reclassify to segment operating results gains of $0.9 million in fiscal 2020 and $0.5 million in fiscal 2021 and thereafter.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "79f0826d2abbaf9504093e1cc562613b", "source_table_id": "cc28d90c509efebe48d4151df5bfda73"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:ba9ff3edc09a64e4f5dac868ba5b3a52", "source": "tatqa", "table_key": "f7fd1932696360bda0b7103d6222013a7a4fecc9", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["Years Ended", "July 27, 2019", "July 28, 2018", "July 29, 2017"], ["Amortization of purchased intangible assets:", "", "", ""], ["Cost of sales", "$624", "$640", "$556"], ["Operating expenses", "", "", ""], ["Amortization of purchased intangible assets", "150", "221", "259"], ["Restructuring and other charges", "—", "—", "38"], ["Total", "$774", "$861", "$853"]]}, "native_split": "test", "question": "Which years does the table provide information for the amortization of purchased intangible assets?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018", "2017"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Amortization of Purchased Intangible Assets\nThe following table presents the amortization of purchased intangible assets (in millions):\nThe decrease in amortization of purchased intangible assets was due largely to the purchased intangible assets related to the divestiture of SPVSS business on October 28, 2018, partially offset by amortization from our recent acquisitions.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "ba9ff3edc09a64e4f5dac868ba5b3a52", "source_table_id": "7b7a7da25f8e700f45fe0bee80fab798"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:dfcc60ee47fcce5f62ded38eac4dc636", "source": "tatqa", "table_key": "4f03cf0b0ed41d421988f8beb0a10d2e8321ffc8", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "2019", "(Restated) 2018"], ["", "£m", "£m"], ["Digital", "327.6", "342.6"], ["Webzone", "6.6", "6.9"], ["Total", "334.2", "349.5"]]}, "native_split": "test", "question": "What were the components making up the total carrying value of CGUs in the table?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Digital", "Webzone"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Impairment test for goodwill\nGoodwill is allocated to the appropriate cash-generating unit (‘CGU’) based on the smallest identifiable group of assets that generates cash inflows independently in relation to the specific goodwill. The recoverable amount of the CGU is determined from value-in-use calculations that use cash flow projections from the latest three-year plan. The carrying value of CGUs is the sum of goodwill, property, plant and equipment and intangibles and is as follows:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "dfcc60ee47fcce5f62ded38eac4dc636", "source_table_id": "44c02185b3d5e6f0a91d9517fd7cdbb6"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:fe4320392585bec48b98562051cbd3e1", "source": "tatqa", "table_key": "e46d82e19d87d10d71ba847105d54c41f7744d03", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "Target Allocations", "Percentage of Plan Assets at December 31,", ""], ["Asset Category", "2020", "2019", "2018"], ["Equity securities", "13%", "13%", "12%"], ["Debt securities", "83%", "83%", "84%"], ["Other", "4%", "4%", "4%"], ["Total", "100%", "100%", "100%"]]}, "native_split": "test", "question": "Which years did the percentage of plan assets for Debt Securities exceed 80% for 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nOur pension plan asset allocation at December 31, 2019, and 2018, and target allocation for 2020 by asset category are as follows:\nWe employ a liability-driven investment strategy whereby a mix of equity and fixed-income investments are used to pursue a derisking strategy which over time seeks to reduce interest rate mismatch risk and other risks while achieving a return that matches or exceeds the growth in projected pension plan liabilities. Risk tolerance is established through careful consideration of plan liabilities and funded status. The investment portfolio primarily contains a diversified mix of equity and fixed-income investments. Other assets such as private equity are used modestly to enhance long-term returns while improving portfolio diversification. Investment risk is measured and monitored on an ongoing basis through quarterly investment portfolio reviews, annual liability measurements, and asset/liability studies at regular intervals.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "fe4320392585bec48b98562051cbd3e1", "source_table_id": "054153aec5a8b7066b1083f3ec3515ed"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:d2a8cd279ae55ad737ce418f4aeec0fe", "source": "tatqa", "table_key": "60be27ee20afd58210826b692931890814193889", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31, 2018"], ["Payroll", "$6,701", "$15,264"], ["Accrued interest", "35,926", "40,903"], ["Accrued voyage expenses", "—", "3,643"], ["Accrued running costs", "—", "42,212"], ["Provision for estimated losses on vessels under time charter", "—", "1,604"], ["Audit fees and related services", "234", "292"], ["Accrued taxes", "8,002", "6,268"], ["Professional fees", "317", "1,251"], ["Other accrued expenses", "—", "12,215"], ["Total accrued expenses", "$51,180", "$123,652"]]}, "native_split": "test", "question": "Which years does the table provide information for Accrued expenses and other liabilities?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nNOTE 10: ACCRUED EXPENSES AND OTHER LIABILITIES\nAccrued expenses and other liabilities as of December 31, 2019 and 2018 consisted of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "d2a8cd279ae55ad737ce418f4aeec0fe", "source_table_id": "1c66f3ce250aa12c64b054c72423134d"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:77c3f23b4666fe29d21d2bf8ce8ec39a", "source": "tatqa", "table_key": "fba8d12228d7f1a23b5330de55eb65f80a62a8d9", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["", "High", "Low", "Dividends\nDeclared Per\nShare"], ["Fiscal Year Ended April 27, 2019", "", "", ""], ["First Quarter", "$45.45", "$37.70", "$0.11"], ["Second Quarter", "41.30", "27.65", "0.11"], ["Third Quarter", "33.98", "20.99", "0.11"], ["Fourth Quarter", "32.22", "25.11", "0.11"], ["Fiscal Year Ended April 28, 2018", "", "", ""], ["First Quarter", "$44.95", "$36.05", "$0.09"], ["Second Quarter", "46.75", "36.75", "0.09"], ["Third Quarter", "48.44", "39.00", "0.11"], ["Fourth Quarter", "42.10", "36.95", "0.11"]]}, "native_split": "test", "question": "In which quarter of 2019 was the high price greater than $40.00?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["First Quarter", "Second Quarter"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities\nOur common stock is traded on the New York Stock Exchange under the symbol \"MEI.\" The following is a tabulation of high and low sales prices for the periods presented and cash dividends declared per share.\nOn June 13, 2019, the Board of Directors declared a dividend of $0.11 per share of common stock, payable on July 26, 2019, to holders of record on July 12, 2019. As of June 18, 2019, the number of record holders of our common stock was 397.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "77c3f23b4666fe29d21d2bf8ce8ec39a", "source_table_id": "ef37215f6a63e5f06c6b32f7c3db7aad"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:f25c6a3983e75fdfa418fda4d6a9cbcb", "source": "tatqa", "table_key": "aeaff78ee919100a5b5f75bb225bacbe113c4507", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "", "Fiscal 2019", "", "Fiscal 2018"], ["", "High", "Low", "High", "Low"], ["First Quarter", "$ 83.14", "$ 63.81", "$ 45.24", "$ 37.43"], ["Second Quarter", "$ 88.08", "$ 70.26", "$ 45.14", "$ 37.55"], ["Third Quarter", "$ 83.95", "$ 54.50", "$ 64.06", "$ 43.24"], ["Fourth Quarter", "$ 78.35", "$ 61.00", "$ 69.75", "$ 52.00"]]}, "native_split": "test", "question": "Which fiscal years does the table provide information for the price range per share of common stock?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities\nThe Company’s common stock is traded on the NASDAQ Stock Market LLC (“NASDAQ”) under the symbol NTAP.\nPrice Range of Common Stock\nThe price range per share of common stock presented below represents the highest and lowest intraday sales prices for the Company’s common stock on the NASDAQ during each quarter of our two most recent fiscal years.\nHolders\nAs of June 7, 2019 there were 413 holders of record of our common stock.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 6, "source_local_id": "f25c6a3983e75fdfa418fda4d6a9cbcb", "source_table_id": "9082b05b2e0e2600d7f405f82795f041"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:9fae782a8fc10120eea8a2d8835b0cba", "source": "tatqa", "table_key": "12d11c10f661b3d4a9c16c04d621094588418e28", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "Year Ended December 31,", "", "% Change"], ["", "2019", "2018", "2019"], ["", "", "(dollars in thousands)", ""], ["Income tax benefit", "$(12,586)", "$(6,653)", "89%"], ["% of pre-tax loss", "39%", "20%", ""]]}, "native_split": "test", "question": "What was the % of pre-tax loss in 2019 and 2018 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["39%", "20%"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Income Tax Provision (Benefit)\nThe income tax benefit for the year ended December 31, 2019 was $12.6 million or approximately 39% of pre-tax loss compared to an income tax benefit of $6.7 million or approximately 20% of pre-tax loss for the year ended December 31, 2018.\nThe income tax benefit for the year ended December 31, 2019 primarily related to the mix of pre-tax income among jurisdictions, discrete tax benefits related to stockbased compensation, and release of certain reserves for uncertain tax positions under ASC 740-10.\nThe income tax benefit for the year ended December 31, 2018 primarily related to a partial release of our valuation allowance and the mix of pre-tax income among jurisdictions, excess tax benefits related to stock-based compensation, and release of uncertain tax positions under ASC 740-10.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "9fae782a8fc10120eea8a2d8835b0cba", "source_table_id": "54336f3b130a313e9b79fc3cd101f4ed"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:4c6369b74a930d8f9061c4fef7889b29", "source": "tatqa", "table_key": "020667b07593a39dee6ca44c5a47b8b4372d6cbb", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["", "", "", "Payments due by period (in thousands)", "", ""], ["", "Total", "Less than 1 year", "1-3 years", "3-5 years", "After 5 years"], ["Notes payable and long-term debt", "$2,496,465", "$375,181", "$491,655", "$1,134,733", "$494,896"], ["Future interest on notes payable and long-term debt(1)", "373,762", "109,506", "142,082", "55,463", "66,711"], ["Operating lease obligations", "603,185", "118,312", "187,644", "114,297", "182,932"], ["Capital lease obligations", "77,829", "6,038", "11,726", "10,928", "49,137"], ["Non-cancelable purchase order obligations(2)", "351,230", "289,516", "61,537", "177", "—"], ["Pension and post retirement contributions and payments(3)", "14,618", "1,135", "1,904", "2,396", "9,183"], ["Other(4)", "77,669", "17,922", "27,863", "14,214", "17,670"], ["Total contractual obligations(5)", "$3,994,758", "$917,610", "$924,411", "$1,332,208", "$820,529"]]}, "native_split": "test", "question": "Which periods does the table provide information for total contractual obligations as of August 31, 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Less than 1 year", "1-3 years", "3-5 years", "After 5 years"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Contractual Obligations\nOur contractual obligations as of August 31, 2019 are summarized below. As disclosed below, while we have certain non-cancelable purchase order obligations for property, plant and equipment, we generally do not enter into non-cancelable purchase orders for materials until we receive a corresponding purchase commitment from our customer. Non-cancelable purchase orders do not typically extend beyond the normal lead time of several weeks, at most. Purchase orders beyond this time frame are typically cancelable.\n(1) Consists of interest on notes payable and long-term debt outstanding as of August 31, 2019. Certain of our notes payable and long-term debt pay interest at variable rates. We have applied estimated interest rates to determine the value of these expected future interest payments.\n(2) Consists of purchase commitments entered into as of August 31, 2019 primarily for property, plant and equipment and software pursuant to legally enforceable and binding agreements.\n(3) Includes the estimated company contributions to funded pension plans during fiscal year 2020 and the expected benefit payments for unfunded pension and postretirement plans from fiscal years 2020 through 2029. These future payments are not recorded on the Consolidated Balance Sheets but will be recorded as incurred.\n(4) Includes (i) a $28.5 million capital commitment, (ii) a $16.2 million obligation related to a new human resource system and (iii) $33.0 million related to the one-time transition tax as a result of the Tax Act that will be paid in annual installments through fiscal year 2026.\n(5) As of August 31, 2019, we have $1.5 million and $103.7 million recorded as a current and a long-term liability, respectively, for uncertain tax positions. We are not able to reasonably estimate the timing of payments, or the amount by which our liability for these uncertain tax positions will increase or decrease over time, and accordingly, this liability has been excluded from the above table.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 7, "source_local_id": "4c6369b74a930d8f9061c4fef7889b29", "source_table_id": "fe0e8fe94bab06428abbca1372dc801f"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:395eb8fd76d91164ae7c37657920dbfd", "source": "tatqa", "table_key": "52d52a74762287a6339cb7696c3b763686806364", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 3, "section_rows": [], "cells": [["", "30 June 2019", "30 June 2018"], ["", "$'000", "$'000"], ["FROM CONTINUING OPERATIONS", "", ""], ["Data centre services revenue", "169,696", "152,560"], ["Interest income", "8,220", "5,778"], ["Distributions from investments", "1,344", "3,191"], ["Subtotal - other revenue", "9,564", "8,969"], ["Total revenue", "179,260", "161,529"], ["Gain on extinguishment of B1 lease", "1,068", "-"], ["Gain on extinguishment of APDC leases", "1,291", "-"], ["Other items included in gains", "675", "284"], ["Total Other income", "3,034", "284"]]}, "native_split": "test", "question": "What does the data centre services revenue consist of?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Recurring monthly service fees", "Upfront project fees"], "answer_type": "multi-span", "answer_from": "text", "paragraphs": "3 Revenue and other income (continued)\n(b) Revenue recognition\nAASB 15 establishes principles for reporting the nature, amount, timing, and uncertainty of revenue and cash flows arising from an entity’s contracts with customers and requires application of a five-step process to identify the contract with the customer, identify performance obligations in the contract, determine transaction price, allocate the transaction price to the performance obligations and recognise revenue when performance obligations are satisfied.\nRevenue is recognised for the major business activities as follows:\n(i) Data centre services\nData centre services revenue primarily consists of recurring monthly service fees and upfront project fees.\nRevenue from the provision of recurring monthly service fees is recognised in the accounting period in which the services are rendered. Project fees are primarily comprised of installation services relating to a customer's initial deployment. As this is not considered to be a distinct service, revenue is deferred and recognised over the term of the contract with the customer, taking into account renewal options that are held by the customer. Upfront discounts provided to customers are contract assets that are amortised over the expected contract life - refer to Note 6(b).\nThe Group applies the practical expedient in the revenue standard and does not disclose information about the transaction price allocated to remaining performance obligations on contracts that are unsatisfied, as the Group has the right to consideration from its customers in an amount that corresponds directly with the value to the customer of the Group’s services to date. This is applied to all its data centre services revenue, on the basis that the upfront project fees are not a significant portion of each contract.\n(ii) Interest income\nInterest income is calculated by applying the effective interest rate to the gross carrying amount of a financial asset, except for financial assets that subsequently become credit-impaired. For credit-impaired financial assets, the effective interest rate is applied to the net carrying amount of the financial asset (after deduction of the loss allowance).\n(iii) Distributions from investments\nDistributions from investments are recognised as revenue when the right to receive payment is established.\nThe following disclosures relate to 30 June 2018 balances:\nRevenue is measured at the fair value of the consideration received or receivable.\nThe Group recognises revenue when the amount of revenue can be reliably measured, it is probable that future economic benefits will flow to the entity and specific criteria have been met for each of the activities as described below. The Group bases its estimates on historical results, taking into consideration the type of customer, the type of transaction and the specifics of each arrangement.\nRevenue is recognised for the major business activities as follows:\n(iv) Data centre services\nRevenue is recognised only when the service has been provided, the amount of revenue can be measured reliably and it is probable that the economic benefits associated with the transaction will flow to the Group. Upfront discounts provided to customers are amortised over the contract term.\n(v) Interest income\nInterest income is recognised using the effective interest method. When a receivable is impaired, the Group reduces the carrying amount to its recoverable amount, being the estimated future cash flow discounted at the original effective interest rate of the instrument, and continues unwinding the discount as interest income. Interest income on impaired loans is recognised using the original effective interest rate.\n(vi) Distributions from investments\nDistributions from investments are recognised as revenue when the right to receive payment is established.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 22, "source_local_id": "395eb8fd76d91164ae7c37657920dbfd", "source_table_id": "f59bd5ee555e5cc8cf1d5223a0145049"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:32c1a1ecb3f1102f18db0cdf4c0c9d17", "source": "tatqa", "table_key": "2970235ad6f81083fc8f7ad199e0bc0fa0519189", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["Consolidated", "", "", ""], ["", "2019", "2018", "Change"], ["Product Sales", "US$’000", "US$’000", "%"], ["Altium Designer licenses", "65,157", "53,088", "23%"], ["Altium Designer subscriptions", "58,959", "53,701", "10%"], ["Octopart search advertising", "17,940", "11,968", "50%"], ["TASKING licenses", "13,536", "10,432", "30%"], ["TASKING maintenance", "8,324", "4,706", "67%"], ["Altium Nexus", "6,277", "3,769", "67%"], ["Service sales", "3,337", "4,624", "(28%)"], ["Other", "3,656", "2,254", "62%"], ["Total Product Sales", "177,216", "144,541", "23%"]]}, "native_split": "test", "question": "What are the segments of product sales?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Altium Designer licenses", "Altium Designer subscriptions", "Octopart search advertising", "TASKING licenses", "TASKING maintenance", "Altium Nexus", "Service sales", "Other"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Operational Highlights\nAltium achieved US$177.2 million in sales (a 23% increase) and US$171.8 million in product revenue (a 23% increase).\nThe Board and Systems business revenue grew to US$126.8 million with all regions reporting positive results. EMEA grew revenue to US$44.6 million, an increase of 15% and continued the transformation of its business model to direct transactional sales in key markets. The America’s achieved revenue of US$50.9 million which was a 14% growth rate. China results were outstanding with revenue at US$19.8 million, a growth rate of 37%. The Altium focus on our business in China led us to open a new sales office in Beijing and to expand our existing sales centres in Shenzhen and Shanghai. The expansion of our footprint in China will enable us to increase our penetration of the market.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "32c1a1ecb3f1102f18db0cdf4c0c9d17", "source_table_id": "9b048dfc08e40405ae69dc2e057dd41a"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:85350abdfeec4b26b548994eb2903b1b", "source": "tatqa", "table_key": "73ebe7ace94d9e331f55fbd38bc9e0b78580d472", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Deferred tax assets:", "", ""], ["Employee benefits", "$5,092", "$5,078"], ["Inventory", "1,649", "1,129"], ["Accounts receivable", "204", "213"], ["Tax credits", "1,300", "1,213"], ["Other assets", "148", "139"], ["Capital Loss carry-forward", "2,455", "1,385"], ["Net operating loss carry-forwards", "5,556", "6,451"], ["Total deferred tax asset", "16,404", "15,608"], ["Deferred tax liabilities:", "", ""], ["Property, plant and equipment", "(1,344 )", "(1,639)"], ["Other liabilities", "(343 )", "(821)"], ["Deferred state income tax", "(767 )", "(727)"], ["Net deferred tax asset", "13,950", "12,421"], ["Valuation allowance", "(13,950 )", "(12,688)"], ["Net deferred tax liability", "$-", "$ (267)"]]}, "native_split": "test", "question": "What is the inventory for 2019 and 2018 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["1,649", "1,129"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "The components of deferred taxes are as follows (in thousands):\nIn assessing the realizability of deferred tax assets, the Company considers whether it is more-likely-than-not that some portion or all of the deferred tax assets will be realized. A valuation allowance, if needed, reduces the deferred tax assets to the amounts expected to be realized. The ultimate realization of deferred tax assets is dependent upon the generation of future taxable income in those periods in which temporary differences become deductible and/or net operating loss carry-forwards can be utilized. We assess all positive and negative evidence when determining the amount of the net deferred tax assets that are more likely than not to be realized. This evidence includes, but is not limited to, prior earnings history, scheduled reversal of taxable temporary differences, tax planning strategies and projected future taxable income. Significant weight is given to positive and negative evidence that is objectively verifiable.\nAs required by the authoritative accounting guidance on accounting for income taxes, the Company evaluates the realizability of its deferred tax assets at each reporting date. Accounting for income taxes requires that a valuation allowance be established when it is more-likely-than-not that all or a portion of the deferred tax assets will not be realized. In circumstances where there is sufficient negative evidence indicating that the deferred tax assets are not more-likely-than-not realizable, the Company establishes a valuation allowance. As of April 30, 2019 and 2018, the Company had a full valuation allowance against its U.S. net deferred tax assets. If these estimates and assumptions change in the future, the Company may be required to reduce its existing valuation allowance resulting in less income tax expense. For the years ended April 30, 2019 and 2018, the valuation allowance increased by approximately $1.3 million and $9.4 million, respectively.\nAs of April 30, 2019, the Company has U.S. federal net operating losses of $23 million of which $4 million begins to expire in Fiscal 2023 through 2031 and which are subject to annual limitation under Internal Revenue Code Section 382. The remaining U.S. federal net operating losses of $18.9 million have an indefinite carry-forward period. The U.S. federal capital loss carry-forward of $9.9 million expires in 2023. The Company also has state net operating loss carry-forwards, R&D tax credits, and state tax credits that expire in various years and amounts.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "85350abdfeec4b26b548994eb2903b1b", "source_table_id": "2d3af3804b56b1097f4ee95efbd4a598"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:b5dd19179326bfbf183b62da6e0da49d", "source": "tatqa", "table_key": "e8649aebb8b7f4ebda2d950cf07edf96d6dd13a8", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended", ""], ["", "", "March 31,", ""], ["", "2019", "2018", "2017"], ["Contract manufacturers and consignment warehouses:       ", "", "", ""], ["Flextronics Technology", "21.8%", "14.0%", "10.4%"], ["Sanmina", "17.7", "16.0", "20.4"], ["Distributors:       ", "", "", ""], ["Avnet Logistics", "31.3", "35.3", "25.5"], ["Nexcomm ", "14.8", "16.1", "19.7"]]}, "native_split": "train", "question": "In which year was Flextronics Technology less than 20.0%?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2018", "2017"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "The following direct customers accounted for 10% or more of our net revenues in one or more of the following periods:\nNokia was our largest customer in fiscal 2019, 2018 and 2017. Nokia purchases products directly from us and through contract manufacturers and distributors. Based on information provided to us by its contract manufacturers and our distributors, purchases by Nokia represented approximately 45%, 36% and 41% of our net revenues in fiscal 2019, 2018 and 2017, respectively. To our knowledge, none of our other OEM customers accounted for more than 10% of our net revenues in any of these periods.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "b5dd19179326bfbf183b62da6e0da49d", "source_table_id": "414c46230d16f755bb66ffddd864bd63"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:e2b021cbbd079e3b385ff81fe136b519", "source": "tatqa", "table_key": "55a3ab34eeb0eea49d87996ed4f3ab1b284929f1", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Trade accounts receivable, net, current (1)", "$375,136", "$335,377"], ["Trade accounts receivable, net, noncurrent (2)", "26,496", "15,948"], ["", "$401,632", "$351,325"]]}, "native_split": "test", "question": "What are the types of accounts in the Company's trade accounts receivable?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Trade accounts receivable, net, current", "Trade accounts receivable, net, noncurrent"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Trade Accounts Receivable\nThe Company’s trade accounts receivable, net, consisted of the following (in thousands):\n(1) Included in “Receivables, net” in the accompanying Consolidated Balance Sheets.\n(2) Included in “Deferred charges and other assets” in the accompanying Consolidated Balance Sheets.\nThe Company’s noncurrent trade accounts receivable result from (1) contracts with customers that include renewal provisions, and (2) contracts with customers under multi-year arrangements. For contracts that include renewal provisions, revenue is recognized up-front upon satisfaction of the associated performance obligations, but payments are received upon renewal. Renewals occur in bi-annual and annual increments over the associated expected contract term, the majority of which range from two to five years. The Company’s contracts with customers under multi-year arrangements generally have three-year terms and are invoiced annually at the beginning of each annual coverage period. The Company records a receivable related to revenue recognized under multi-year arrangements as the Company has an unconditional right to invoice and receive payment in the future related to these arrangements.\nWhere the timing of revenue recognition differs from the timing of invoicing and payment, the Company has determined that its contracts do not include a significant financing component. A substantial amount of the consideration promised by the customer under the contracts that include renewal provisions is variable, and the amount and timing of that consideration varies based on the occurrence or nonoccurrence of future events that are not substantially within the Company’s control. With respect to multi-year year arrangements, there is minimal difference between the consideration received and the cash selling price, any offered discounts are driven by volume, and the contracts are of short duration resulting in insignificant interest. Thus, the primary purpose of the invoicing terms on the multi-year arrangements is to provide the customer with a simplified and predictable way of purchasing certain products, not to provide financing or to receive financing from the Company’s customer.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 6, "source_local_id": "e2b021cbbd079e3b385ff81fe136b519", "source_table_id": "aad7041cfc521e55841bb2b62200bad1"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:607dd25b10e5d14396ef2abda187330d", "source": "tatqa", "table_key": "63b72a3ad354e2812fc6d90c9e1e0d50bc6461d6", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 3, "section_rows": [], "cells": [["", "June 30,", ""], ["", "2019", "2018"], ["Deferred tax assets", "", ""], ["Non-capital loss carryforwards", "$161,119", "$129,436"], ["Capital loss carryforwards", "155", "417"], ["Undeducted scientific research and development expenses", "137,253", "123,114"], ["Depreciation and amortization", "683,777", "829,369"], ["Restructuring costs and other reserves", "17,845", "17,202"], ["Deferred revenue", "53,254", "62,726"], ["Other", "59,584", "57,461"], ["Total deferred tax asset", "$1,112,987", "$1,219,725"], ["Valuation Allowance", "$(77,328)", "$(80,924)"], ["Deferred tax liabilities", "", ""], ["Scientific research and development tax credits", "$(14,482)", "$(13,342)"], ["Other", "(72,599)", "(82,668)"], ["Deferred tax liabilities", "$(87,081)", "$(96,010)"], ["Net deferred tax asset", "$948,578", "$1,042,791"], ["Comprised of:", "", ""], ["Long-term assets", "1,004,450", "1,122,729"], ["Long-term liabilities", "(55,872)", "(79,938)"], ["", "$948,578", "$1,042,791"]]}, "native_split": "test", "question": "What are the Fiscal years included in the table?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "The primary components of the deferred tax assets and liabilities are as follows, for the periods indicated below:\nWe believe that sufficient uncertainty exists regarding the realization of certain deferred tax assets that a valuation allowance is required. We continue to evaluate our taxable position quarterly and consider factors by taxing jurisdiction, including but not limited to factors such as estimated taxable income, any historical experience of losses for tax purposes and the future growth of OpenText.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "607dd25b10e5d14396ef2abda187330d", "source_table_id": "b3d63fb06110ad7e91c9e765227c1d27"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:4e47154be32857052c94badee723e437", "source": "tatqa", "table_key": "a76b614601520e103e1060ef27cdc5e9a30a2f58", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 5, "section_rows": [], "cells": [["(Dollars in Millions)", "April 28, 2018", "April 29, 2017", "Net Change ($)", "Net Change (%)"], ["Net Sales", "$ 0.3", "$ 0.2", "$ 0.1", "50.0 %"], ["Gross Profit", "$ (3.5)", "$ (3.1)", "$ (0.4)", "(12.9)%"], ["Loss from Operations", "$ (11.4)", "$ (8.5)", "$ (2.9)", "(34.1)%"]]}, "native_split": "test", "question": "How much was the Medical segment gross profit in fiscal 2018 and 2017 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$ (3.5)", "$ (3.1)"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Medical Segment Results\nBelow is a table summarizing results for the fiscal years ended:\nNet Sales. The Medical segment had minimal net sales in both periods from newly launched products.\nGross Profit. Medical segment gross profit was a loss of $3.5 million in fiscal 2018 compared to a loss of $3.1 million in fiscal 2017. The increased loss primarily relates to the vertical manufacturing integration of some key components and research efforts to expand the product offerings.\nLoss from Operations. Medical segment loss from operations increased $2.9 million to $11.4 million in fiscal 2018 compared to $8.5 million in fiscal 2017. The increased loss relates to higher outside professional fees, research and development and marketing expenses in fiscal 2018. Financial Condition, Liquidity and Capital Resources", "scale": "million", "scoring": "set_match", "meta": {"paragraph_count": 5, "source_local_id": "4e47154be32857052c94badee723e437", "source_table_id": "1380a11c3e0c1ebff7c19af6d1c014ac"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:b2896adc0016974ef2559c4bbe3154d8", "source": "tatqa", "table_key": "8492e9a671644d660abec22a725d2eaafe2e04c9", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "Fiscal year-end", ""], ["", "2019", "2018"], ["Purchased parts and assemblies", "$134,298", "$137,566"], ["Work-in-process", "174,550", "186,240"], ["Finished goods", "133,682", "162,935"], ["Total inventories", "$442,530", "$486,741"]]}, "native_split": "test", "question": "What are the components factored in total inventories?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Purchased parts and assemblies", "Work-in-process", "Finished goods"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Inventories\nInventories are stated at the lower of cost (first-in, first-out or weighted average cost) or net realizable value. Inventories are as follows (in thousands):", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "b2896adc0016974ef2559c4bbe3154d8", "source_table_id": "b09a98e495cb121ce234c632da6fd5ab"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:96035218ccce75bb4fb942af908495bd", "source": "tatqa", "table_key": "ae42aea7261afeba062b232c4e6b65212c2ab96b", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 4, "section_rows": [], "cells": [["All figures in USD except number of shares and earnings (loss) per common share", "2019", "2018", "2017"], ["Numerator:      ", "", "", ""], ["Net Loss  ", "(10,352)", "(95,306)", "(204,969)"], ["Denominator:            ", "", "", ""], ["Basic - Weighted Average Common Shares Outstanding  ", "142,571,361", "141,969,666", "103,832,680"], ["Dilutive – Weighted Average Common Shares Outstanding  ", "142,571,361", "141,969,666", "103,832,680"], ["Loss per Common Share:            ", "", "", ""], ["Basic  ", "(0.07)", "(0.67)", "(1.97)"], ["Diluted  ", "(0.07)", "(0.67)", "(1.97)"]]}, "native_split": "test", "question": "What are the respective basic weighted average common shares outstanding in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["141,969,666", "142,571,361"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "13. EARNINGS (LOSS) PER SHARE\nBasic earnings per share (“EPS”) are computed by dividing net income (loss) by the weighted-average number of common shares outstanding for the period. Diluted EPS is computed by dividing net income by the weighted-average number of common shares and dilutive common stock equivalents outstanding during the period.\nOn March 29, 2019, the Company launched an ATM program of our common shares for up to $40.0 million. The Company has issued 5,260,968 shares with net proceeds of $17.9 million under its At-the-Market as of December 31, 2019. The Company has not issued any shares subsequent to the balance sheet date.\nThe remaining available proceeds through the offering is $21.4 million as of the date of this report. Based on the share price of the Company of $3.47 per share as of April 3, 2020 it would have resulted in 6,173,500 new shares being issued, if fully utilizing the remaining balance available through the ATM.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "96035218ccce75bb4fb942af908495bd", "source_table_id": "734df6cfe7b18ff15734dd10c3d8e4b3"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:e299f45a24bf8149e3d2db179e764ac4", "source": "tatqa", "table_key": "eb8f6d6da4b187b00125f2e5a499a821dfcae3b6", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["(In thousands)", "December 31,", ""], ["", "2019", "2018"], ["Gross accounts receivables", "$ 21,193", "$ 14,135"], ["Allowance for returns and doubtful accounts", "(265 )", "(277 )"], ["Unpaid portion of deferred revenue", "(10,847 )", "(10,670 )"], ["Note receivable", "458", "458"], ["Allowance for note receivable", "(458 )", "(458 )"], ["Receivables, net", "$ 10,081", "$ 3,188"]]}, "native_split": "test", "question": "What is the net receivables for 2019 and 2018 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["10,081", "3,188"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "5. Receivables, net\nThe allowance for doubtful accounts includes all specific accounts receivable which we believe are likely not collectable based on known information. The reduction for the unpaid portion of deferred revenue represents future customer service or maintenance obligations which have been billed to customers, but remain unpaid as of the respective balance sheet dates. Deferred revenue on our consolidated balance sheets represents future customer service or maintenance obligations which have been billed and collected as of the respective balance sheet dates.\nThe note receivable represents the remaining outstanding balance of an original note related to the sale of a product line in 2005 in the amount of $540 thousand. This was fully reserved at the time of the sale as the note’s collectability was not assured. The note receivable is fully reserved at December 31, 2019 and 2018.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "e299f45a24bf8149e3d2db179e764ac4", "source_table_id": "fed3ce526008faeb1ce01e1602cd077f"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:dce800aa37e3a6c06890c0e53b831172", "source": "tatqa", "table_key": "b0e749b6bebdac5aab2ff74bcb8b3beabc325261", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 6, "section_rows": [], "cells": [["Remuneration key performance indicator", "2019 actual", "2019 threshold", "2019 target", "2019 maximum", "Remuneration measure"], ["Group operating profit (£m)", "277.3", "256.7", "270.3", "283.8", "Annual Incentive Plan"], ["Group cash generation (£m)", "296.4", "270.7", "285.0", "299.2", "Annual Incentive Plan"], ["Group ROCE (%)", "54.5", "50.1", "52.7", "55.3", "Annual Incentive Plan"], ["2017-2019 EPS (%)", "57.5", "27.6", "N/A", "52.3", "Performance Share Plan"], ["2017-2019 relative TSR (percentile TSR)", "94th", "50th", "N/A", "75th", "Performance Share Plan"]]}, "native_split": "test", "question": "What are the plans under remuneration measure?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Annual Incentive Plan", "Performance Share Plan"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "5. Remuneration continued\nRemuneration at a glance 2019\nHow we performed\nThe above figures exclude the acquisition of Thermocoax.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "dce800aa37e3a6c06890c0e53b831172", "source_table_id": "5e5f8b76df669b1a32df3074c2c551be"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:c8bfdeb5fcef1b85fc44a5574b0664db", "source": "tatqa", "table_key": "f0e95d229ec0dca0febf48284a1c26f7837433ce", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Land", "$773,068", "$773,068"], ["Buildings and improvements", "12,574,893", "12,206,908"], ["Warehouse equipment", "15,011,605", "13,424,236"], ["Furniture, fixtures and leasehold improvements", "13,155,606", "12,018,984"], ["Vehicles", "3,687,901", "3,229,551"], ["Construction in progress", "617,881", "743,278"], ["", "45,820,954", "42,396,025"], ["Less accumulated depreciation and amortization:", "(28,165,539)", "(26,627,541)"], ["Owned property and equipment", "$ 17,655,415", "$ 15,768,484"]]}, "native_split": "test", "question": "What are the company's respective warehouse equipment value in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["13,424,236", "15,011,605"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "4. PROPERTY AND EQUIPMENT, NET:\nProperty and equipment at September 2019 and September 2018 consisted of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "c8bfdeb5fcef1b85fc44a5574b0664db", "source_table_id": "2145fa4a784df129c0fcf87f7542d442"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:4dca910fcbd20a2ed517ecba4cf1c0bc", "source": "tatqa", "table_key": "f0e95d229ec0dca0febf48284a1c26f7837433ce", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Land", "$773,068", "$773,068"], ["Buildings and improvements", "12,574,893", "12,206,908"], ["Warehouse equipment", "15,011,605", "13,424,236"], ["Furniture, fixtures and leasehold improvements", "13,155,606", "12,018,984"], ["Vehicles", "3,687,901", "3,229,551"], ["Construction in progress", "617,881", "743,278"], ["", "45,820,954", "42,396,025"], ["Less accumulated depreciation and amortization:", "(28,165,539)", "(26,627,541)"], ["Owned property and equipment", "$ 17,655,415", "$ 15,768,484"]]}, "native_split": "test", "question": "What are the company's respective vehicles value in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["3,229,551", "3,687,901"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "4. PROPERTY AND EQUIPMENT, NET:\nProperty and equipment at September 2019 and September 2018 consisted of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "4dca910fcbd20a2ed517ecba4cf1c0bc", "source_table_id": "2145fa4a784df129c0fcf87f7542d442"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:227a0357e3487e7cd5ff15b8d86b1045", "source": "tatqa", "table_key": "e46d82e19d87d10d71ba847105d54c41f7744d03", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "Target Allocations", "Percentage of Plan Assets at December 31,", ""], ["Asset Category", "2020", "2019", "2018"], ["Equity securities", "13%", "13%", "12%"], ["Debt securities", "83%", "83%", "84%"], ["Other", "4%", "4%", "4%"], ["Total", "100%", "100%", "100%"]]}, "native_split": "test", "question": "Which years does the table provide information for the projected benefit obligation, accumulated benefit obligation and fair value of plan assets for the company's Pension Plans?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2020", "2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nOur pension plan asset allocation at December 31, 2019, and 2018, and target allocation for 2020 by asset category are as follows:\nWe employ a liability-driven investment strategy whereby a mix of equity and fixed-income investments are used to pursue a derisking strategy which over time seeks to reduce interest rate mismatch risk and other risks while achieving a return that matches or exceeds the growth in projected pension plan liabilities. Risk tolerance is established through careful consideration of plan liabilities and funded status. The investment portfolio primarily contains a diversified mix of equity and fixed-income investments. Other assets such as private equity are used modestly to enhance long-term returns while improving portfolio diversification. Investment risk is measured and monitored on an ongoing basis through quarterly investment portfolio reviews, annual liability measurements, and asset/liability studies at regular intervals.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "227a0357e3487e7cd5ff15b8d86b1045", "source_table_id": "054153aec5a8b7066b1083f3ec3515ed"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:5c370f07c571d9d9d2240a6adb9cdb5a", "source": "tatqa", "table_key": "602eeac4863db06e1f7dcc8eafe7fad492622ac4", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["All figures in USD ‘000 ", "2019", "2018"], ["Accrued Interest  ", "163", "1,598"], ["Accrued Expenses  ", "11,569", "7,362"], ["Settlement Deferred Compensation Liabilities  ", "3,830", "-"], ["Total as of December 31,  ", "15,562", "8,960"]]}, "native_split": "test", "question": "What are the respective accrued expense in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["7,362", "11,569"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "12. ACCRUED LIABILITIES\nThe settlement of the deferred compensation liabilities includes the settlement with our former CFO and Executive Vice President that is payable within March 31, 2020 and payroll taxes related to this settlement and the settlement of the Executive Pension Plan with our Chairman, President & CEO. We refer to note 7 for further information.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "5c370f07c571d9d9d2240a6adb9cdb5a", "source_table_id": "7618a93f40fbba431a7543cb1587330e"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:fce3ac15509507035f7d0488fa1ec66b", "source": "tatqa", "table_key": "f0e95d229ec0dca0febf48284a1c26f7837433ce", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Land", "$773,068", "$773,068"], ["Buildings and improvements", "12,574,893", "12,206,908"], ["Warehouse equipment", "15,011,605", "13,424,236"], ["Furniture, fixtures and leasehold improvements", "13,155,606", "12,018,984"], ["Vehicles", "3,687,901", "3,229,551"], ["Construction in progress", "617,881", "743,278"], ["", "45,820,954", "42,396,025"], ["Less accumulated depreciation and amortization:", "(28,165,539)", "(26,627,541)"], ["Owned property and equipment", "$ 17,655,415", "$ 15,768,484"]]}, "native_split": "test", "question": "What are the company's respective land value in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$773,068", "$773,068"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "4. PROPERTY AND EQUIPMENT, NET:\nProperty and equipment at September 2019 and September 2018 consisted of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "fce3ac15509507035f7d0488fa1ec66b", "source_table_id": "2145fa4a784df129c0fcf87f7542d442"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:06db77f85f3558fa8de6466de9e3cb78", "source": "tatqa", "table_key": "e5b698627364518b6504faee4ada5fce9b8b0498", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "", "Fiscal Years"], ["", "2019", "2018", "2017"], ["Revenue by geography:", "", "", ""], ["Asia Pacific (1)", "$3,049", "$4,905", "$5,810"], ["Europe", "2,459", "1,280", "2,015"], ["North America (2)", "4,802", "6,444", "4,324"], ["Total revenue", "$10,310", "$12,629", "$12,149"]]}, "native_split": "test", "question": "What is the respective revenue from Asia Pacific in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$4,905", "$3,049"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "The following is a breakdown of revenue by shipment destination (in thousands):\n(1) Asia Pacific includes revenue from China $1.1 million or 11% and Japan of $1.8 million or 17% of total revenue in 2019 and $1.8 million or 15% and $1.6 million or 12% of total revenue in 2018, respectively. In 2017, revenue from China and Japan were $1.3 million or 11% and $1.5 million or 12%, respectively.\n(2) North America includes revenue from the United States of $4.7 million or 46% of total revenue in 2019, $6.4 million or 50% of total revenue in 2018 and $4.2 million or 34% of total revenue in 2017.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "06db77f85f3558fa8de6466de9e3cb78", "source_table_id": "bf3bb2fe848111c6f1370b66cdd8da3e"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:89185044a4b3bc7f5f343c17f8d67b52", "source": "tatqa", "table_key": "029cf761e55ad0fc7d14d75131863053698d5824", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 5, "section_rows": [], "cells": [["(Dollars in thousands)", "Year Ended December 31,", "", "Period-To-Period Change", ""], ["", "2019", "2018", "2019 vs. 2018", ""], ["", "Amount", "Amount", "Amount", "Percentage"], ["Software-as-a-service related", "$ 72,831", "$ 65,646", "7,185", "10.9 %"], ["Software and hardware related", "8,015", "4,534", "3,481", "76.8 %"], ["Maintenance", "10,519", "9,834", "685", "7.0 %"], ["Total software-related revenues", "91,365", "80,014", "11,351", "14.2 %"], ["Sequencing and molecular analysis", "1,733", "3,129", "(1,396)", "(44.6)%"], ["Home health care services", "2,863", "6,321", "(3,458)", "(54.7)%"], ["Total net revenue", "$ 95,961", "$ 89,464", "$ 6,497", "7.3 %"]]}, "native_split": "test", "question": "What are the respective maintenance revenue in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["9,834", "10,519"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Comparison of the years ended December 31, 2019 and 2018\nRevenue\nTotal revenue increased $6.5 million, or 7.3%, from $89.5 million for the year ended December 31, 2018 to $96.0 million for the year ended December 31, 2019. Our total revenue growth was driven primarily by growth in our SaaS, software and hardware, and maintenance categories partially offset by decreases in our home health care services and sequencing and molecular analysis revenue categories.\nSaaS revenue was $72.8 million for the year ended December 31, 2019, an increase of $7.2 million, or 10.9%, from $65.6 million for the year ended December 31, 2018. This growth was due to a $4.0 million increase from Eviti platform solutions related to the combination of new customers and increased covered lives on existing customers and a $3.2 million increase in NaviNet SaaS revenue, largely from the higher value of professional services projects being completed in 2019.\nSoftware and hardware revenue increased $3.5 million, or 76.8% from $4.5 million in the year ended December 31, 2018 to $8.0 million in the year ended December 31, 2019. The main contributing factor for this increase was the timing of a large DCX customer contract completed and recognized in the current year. Our software and hardware related revenue results experience fluctuations due to the timing of implementation completions for our DCX customers and our revenue recognition for those arrangements.\nMaintenance revenue increased $0.7 million, or 7.0%, from $9.8 million in the year ended December 31, 2018 to $10.5 million for the year ended December 31, 2019. This increase was due to the timing of DCX customer contracts and post contract support maintenance services completed and recognized in the current year period.\nSequencing and molecular analysis revenue decreased $1.4 million, or 44.6% from $3.1 million for the year ended December 31, 2018 to $1.7 million for the year ended December 31, 2019. This decrease reflected lower volume of GPS samples sequenced and recognized as revenue in the current year resulting from deliveries for patients covered by contract and non-contracted payers. Currently, we recognize revenue from clients with executed contracts, and from clients without a contractual agreement where we recognize revenue on a cash basis given the uncertainty over reimbursement. As we gain additional insurance coverage, including coverage under government insurance programs, we expect to be able to reduce the portion of sequencing and molecular analysis revenue which is recognized on a cash basis.\nWe continue to focus efforts to enhance reimbursement from plans when profiles are ordered and there is no payer contract in place. We are actively engaging plans with detail which supports a physician’s reason for ordering. Our utilization of pre-authorizations and supporting documentation assists in the overall billing and appeal process, optimizing payment with payers, who do not have a formal agreement with us.\nIn parallel with the private payer activities described above, we are also making extensive efforts to explore approval pathways for our test capabilities (including the FDA in-vitro medical device clearance we received in November 2019), which we believe will facilitate coverage from governmental programs such as Medicare. Those activities are ongoing but have uncertainty on the timelines as to formal approval. Lastly, we have implemented an increase in the patient financial responsibility which is collected prior to testing to ensure that at least a partial payment is received for every test performed, we expect unpaid and partial paid orders to decline, which will likely result in a decline in total GPS orders and revenue in the short-term.\nHome health care services revenue decreased $3.5 million, or 54.7%, from $6.3 million in 2018 to $2.9 million for the year ended December 31, 2019. This decrease was due to the sale of our home health care services business in June 2019.\nWe believe that significant opportunities exist for expanded cross-selling across our products and across our existing customer base, including Eviti and NaviNet customer bases. We also believe that our customer base and our product solutions provide unique opportunities to expand the volume of GPS Cancer analysis reporting to our customer base. Maintaining our current customer base will be important to our future SaaS recurring revenue streams.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 11, "source_local_id": "89185044a4b3bc7f5f343c17f8d67b52", "source_table_id": "58eaf446298eec4e8099090d51e77cec"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:0103b1e44ef0047d788e2cd47d5e7f8c", "source": "tatqa", "table_key": "e3b1799127ce0f0cd7c1ffab82bb2a894a5723ac", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "", "For the Year Ended"], ["", "", "December 31,"], ["", "2019", "2018"], ["", "", "(in thousands)"], ["Zyla (Oxaydo)", "$351", "$386"], ["MainPointe (Nexafed)", "21", "24"], ["Royalty revenues", "372", "$410"]]}, "native_split": "test", "question": "What are the two forms of revenue generated from contracts with customers?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Sales-based Milestones and Royalty Revenues", "License and Collaboration Agreement Revenues"], "answer_type": "multi-span", "answer_from": "text", "paragraphs": "NOTE 4 – REVENUE FROM CONTRACTS WITH CUSTOMERS\nRevenue is recognized when, or as, performance obligations under terms of a contract are satisfied, which occurs when control of the promised service is transferred to a customer. Revenue is measured as the amount of consideration the Company expects to receive in exchange for transferring services to a customer (“transaction price”). The Company will then recognize as revenue the amount of the transaction price that is allocated to the respective performance obligation when, or as, the performance obligation is satisfied. When determining the transaction price of the contract, an adjustment is made if payment from a customer occurs either significantly before or significantly after performance, resulting in a significant financing component. None of the Company’s licenses and collaboration agreements contained a significant financing component at either December 31, 2019 or 2018.\nThe Company’s existing license and collaboration agreements may contain a single performance obligation or may contain multiple performance obligations. Those which contain multiple performance obligations will require an allocation of the transaction price based on the estimated relative standalone selling prices of the promised services underlying each performance obligation.\nThe Company’s existing license and collaboration agreements contain customer options for the license of additional products and territories. We determined the option’s standalone selling prices based on the option product’s potential market size in the option territory as compared to the currently licensed product and U.S. territory. Some of our existing license and collaboration agreements contain a license to the technology as well as licenses to tradenames or trademarks. The Company determined that the licenses to the tradenames or trademarks were immaterial in context of the contract.\nSales-based Milestones and Royalty Revenues\nThe commercial sales-based milestones and sales royalties earned under the license and collaboration for Oxaydo and sales royalties earned under the license for the Nexafed products, are recorded in the period of the related sales by Zyla and MainPointe. Payments of sales-based milestones are generally due within 30 days after the end of a calendar year. Payments of royalties are generally due within 45 days after the end of a calendar quarter.\nLicense and Collaboration Agreement Revenues\nThe achievement of milestones under the Company’s license and collaboration agreements will be recorded as revenue during the period the milestone’s achievement becomes probable, which may result in earlier recognition as compared to the previous accounting standards. The license fee of an option product or option territory under the Company’s license and collaboration agreements will be recorded as revenue when the option is exercised and any obligations on behalf of the Company, such as to transfer know-how, has been fulfilled. The monthly license fee under the Company’s LTX-03 license and collaboration agreement will be recorded as revenue upon the fulfillment of the monthly development activities. The out-of-pocket development expenses under the license and collaboration agreements will be recorded as revenue upon the performance of the service or delivery of the material during the month.\nOn June 28, 2019 we entered into an agreement with AD Pharma for the development and license of LTX-03 (hydrocodone bitartrate with acetaminophen) immediate-release tablets utilizing Acura’s patented LIMITx™ having a monthly license payment of $350 thousand from AD Pharma to us for a period of up to 18 months until November 2020. AD Pharma will pay directly for or reimburse Acura to the extent Acura pay’s for, all out-of-pocket development expenses. The first license payment was received July 2, 2019.\nDisaggregation of Total Revenues\nThe Company has two license agreements for currently marketed products containing its technologies; the Oxaydo product containing the Aversion Technology has been licensed to Zyla and the Nexafed products containing the Impede Technology which have been licensed to MainPointe. On January 1, 2020, MainPointe assigned to AD Pharma, with Acura’s consent, all of its right, title and interest in the MainPointe Agreement between MainPointe and Acura. All of the Company’s royalty revenues are earned from these two license agreements by the licensee’s sale of products in the United States.\nRoyalty revenues by licensee are summarized below:\nContract Balance and Performance Obligations\nThe Company had no contract assets and contract liability balances under the license and collaboration agreements at either December 31, 2019 or 2018. Contract assets may be reported in future periods under prepaid expenses or other current assets on the consolidated balance sheet. Contract liabilities may be reported in future periods consisting of deferred revenue as presented on the consolidated balance sheet.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 14, "source_local_id": "0103b1e44ef0047d788e2cd47d5e7f8c", "source_table_id": "22be2fdb6d29626851b25f7a53fada57"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:16def2f8df24e64f8e415c8a208153c5", "source": "tatqa", "table_key": "750b0ecb7cb35291d78f8e11bfdee47df73748ed", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 3, "section_rows": [], "cells": [["", "Years Ended December 31", ""], ["", "2019", "2018"], ["Salaries, Wages and Benefits ", "$8,471,278", "$5,823,057"], ["Professional Fees  ", "850,086", "362,678"], ["Acquisition Related Costs  ", "799,623", "607,670"], ["Board Compensation  ", "137,000", "144,125"], ["Investor Relations  ", "105,639", "113,059"], ["Consultants  ", "245,386", "167,694"], ["Advertising and Promotion  ", "709,006", "299,955"], ["Depreciation and Amortization  ", "1,282,786", "316,502"], ["Research, Development, and Maintenance  ", "2,672,406", "675,660"], ["Integration Incentives  ", "208,855", "132,500"], ["Office, Facility and Other  ", "695,493", "472,250"], ["Travel  ", "695,283", "390,563"], ["   Subtotal  ", "16,872,841", "9,505,713"], ["Stock-based Compensation  ", "2,260,298", "2,520,852"], ["Total Operating Expense ", "$19,133,139", "$12,026,565"]]}, "native_split": "test", "question": "What were the total operating expenses during fiscal years 2018 and 2019, respectively? ", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$12,026,565", "$19,133,139"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Operating Expenses\nOperating expenses increased to approximately $19.1 million for the year ended December 31, 2019, from approximately $12.0 million for the year ended December 31, 2018, an increase of approximately 60%. The detail by major category is reflected in the table below.\nThe main drivers for the overall increase in operating expenses in 2019 was our focus on staffing and scaling our company to foster, and be able to support, accelerated revenue growth.\nWithin the operating expenses, there were a variety of increases, the largest of which was in salaries, wages and benefits, as a result of additional staff added in 2018 and 2019, including related benefits. During 2019, we hired a chief commercial officer, a chief technology officer, five new salespeople, a human resources manager, as well as other administrative positions. We also added 14 employees as a result of our RMDY acquisition in October 2019. During 2018, we added to our staff in several key areas, including a head of data analytics, an additional VP of sales, and a controller. We also added 10 employees in late 2018 as a result of our CareSpeak acquisition. The full year impact of these 2018 hires also increased payroll expense in 2019. We expect our compensation expense to increase in 2020, but at a much lower rate than in 2019.\nProfessional fees increased primarily because of costs associated with our uplisting to Nasdaq and the completion of the underwritten offering, as well as ongoing compliance with Sarbanes Oxley. We also switched auditors in 2019, which resulted in higher audit fees.\nAcquisition costs are related to our acquisitions of RMDY Health in 2019 and CareSpeak Communications in October 2018. These costs include investment banker fees, legal and accounting due diligence, audit costs associated with CareSpeak, valuation experts for the purchase price allocation, and other miscellaneous costs. Since RMDY Health was a larger company than CareSpeak Communications, the costs associated with the acquisition were higher.\nBoard compensation decreased slightly from 2018 to 2019 as we had five independent directors for a portion of 2018, as opposed to the four that we had in 2019.\nThe cost of consultants increased from 2018 to 2019. The primary reason for the increase was related to consultants used for quality certifications, as well as for marketing activities.\nOur advertising and promotion costs increased significantly from 2018 to 2019 as a result of increased marketing activities. This included increased attendance and sponsorship at conferences, rebranding, and other marketing activities.\nExpenses related to research, development, management, and maintenance of our technology increased in 2019 primarily as a result of research into potential new product areas.\nIntegration incentives, which are fees paid to accelerate access to new partners, increased in 2019, as we launched with a greater number of new EHRs in 2019 than in 2018.\nDepreciation and amortization increased significantly in 2019 from the 2018 levels. The increased amortization resulting from the acquisition of CareSpeak and the resulting intangible assets were amortized for a full year in 2019 as opposed to only the fourth quarter of 2018. We also had three months of amortization related to the intangible assets acquired as part of the acquisition of RMDY in October 2019. We expect depreciation and amortization expense in 2020 to increase over 2019 levels due to the full year of amortization of RMDY intangibles.\nOffice, facility, and other costs increased from 2018 to 2019. The main reason for the change related to a higher level of activity with more employees and increased expenses resulting from the RMDY acquisition.\nStock based compensation decreased by approximately $260,000 from $2.5 million in 2018 to $2.3 million in 2019 primarily because performance-based awards granted in 2018 vested, whereas performance-based awards granted in 2019 did not vest because we did not meet the stretch goals required for vesting.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 14, "source_local_id": "16def2f8df24e64f8e415c8a208153c5", "source_table_id": "2eb70b081b9d6349d0c5a5c667689fbd"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:22725b70cdd0ea32e99476e0edeceda4", "source": "tatqa", "table_key": "2b63987f798054471d69ed9a322f0bc0b9956964", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["", "Fiscal 2019 (2)", "Fiscal 2018(1)(2)", "Fiscal 2017(2)", "Fiscal 2016 (2)(3)(5)", "Fiscal 2015(2)(4)"], ["", "", "", "(Dollars in thousands, except per share data)", "", ""], ["Consolidated Statements of Operations Data:", "", "", "", "", ""], ["Revenues", "$589,464", "$529,675", "$548,441", "$383,881", "$282,358"], ["Gross profit", "237,496", "210,339", "215,597", "102,682", "85,738"], ["Net income (loss)", "39,346", "104,036", "40,913", "(6,557)", "(1,523)"], ["Basic net income (loss) per share", "0.52", "1.42", "0.57", "(0.10)", "(0.03)"], ["Diluted net income (loss) per share", "0.51", "1.38", "0.55", "(0.10)", "(0.03)"], ["Consolidated Balance Sheets Data:", "", "", "", "", ""], ["Cash, cash equivalents and marketable securities", "$220,872", "$149,003", "$140,172", "$108,905", "$187,589"], ["Working capital", "282,483", "235,302", "213,693", "172,002", "214,437"], ["Total assets", "839,882", "728,222", "646,574", "618,982", "342,723"], ["Term loan, net of current portion", "15,639", "34,971", "87,228", "125,475", "—"], ["Total stockholders' equity", "640,997", "580,164", "458,637", "401,056", "294,681"], ["Number of employees", "1,836", "1,676", "1,685", "1,571", "958"]]}, "native_split": "test", "question": "What is the Gross Profit in 2019, 2018 and 2017 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["237,496", "210,339", "215,597"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Item 6: Selected Financial Data\nThe following selected consolidated financial data is derived from our consolidated financial statements. This data should be read in conjunction with our consolidated financial statements and the related notes, and Item 7: Management's Discussion and Analysis of Financial Condition and Results of Operations contained elsewhere in this Annual Report on Form 10-K.\n(1) Fiscal 2018 net income includes an income tax benefit of $75.8 million from a valuation allowance release against certain U.S. deferred tax assets. See Note 13 of Notes to Consolidated Financial Statements. (2) Fiscal 2019, 2018, 2017, 2016, and 2015 net income (loss) includes restructuring charges, net, of $0.5 million, $0.2 million, $0.8 million, $7.3 million and $0.6 million, respectively.\n(3) Fiscal 2016 net loss includes impairment charges of $12.4 million. (4) Fiscal 2015 net loss includes the following: i) a $1.5 million gain from a business interruption insurance claim relating to a factory fire at a customer's facility; and ii) a $1.0 million net gain from the sale of intellectual property.\n(5) Fiscal 2016 includes the following as a result of the Cascade Microtech acquisition: i) $82.6 million in revenue; ii) $27.8 million of intangible amortization expense; and iii) a $7.6 million charge for inventory-related step-up amortization.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 5, "source_local_id": "22725b70cdd0ea32e99476e0edeceda4", "source_table_id": "a70e7b04b9667116a9dd83ba84596055"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:b06bbb5b19641a0d52f8789300eb4d6f", "source": "tatqa", "table_key": "0e9520752afa6ea7e995b3c5abfc78e3721606b7", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended", ""], ["", "January 3, 2020", "December 28, 2018", "December 29, 2017"], ["Expected volatility", "22.02%", "25.37%", "27.19%"], ["Risk free rate of return", "2.39%", "2.35%", "1.53%"], ["Weighted average grant date stock price", "$62.66", "$65.00", "$53.73"]]}, "native_split": "test", "question": "In which year was Risk free rate of return more than 2.0%?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2020", "2018"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "The Monte Carlo simulation assumptions used for the periods presented were as follows:\nAs of January 3, 2020, there was $12 million of unrecognized compensation cost, net of estimated forfeitures, which is expected to be recognized over a weighted average period of 1.7 years. The fair value of performance-based stock awards that vested in fiscal 2019, 2018 and 2017 was $9 million, $13 million, and $4 million, respectively.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "b06bbb5b19641a0d52f8789300eb4d6f", "source_table_id": "6665869af8770e824f609a633f9a85a2"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:595d82dc74f20aabd4c6c3a7c7ac667e", "source": "tatqa", "table_key": "3856f3e1b3981ece466ace66c3ffb1bc60b818c1", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "Years Ended December 31,", "", "Change", ""], ["", "2019", "2018", "$", "%"], ["", "", "(dollars in thousands)", "", ""], ["Cost of revenues", "$22,843", "$27,154", "$(4,311)", "(16)%"], ["Gross profit", "26,193", "31,477", "(6,284)", "(17)"], ["Gross margin", "53%", "54%", "", ""]]}, "native_split": "test", "question": "What is the cost of revenues for 2019 and 2018 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$22,843", "$27,154"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Cost of Revenues and Gross Margin\nCost of revenues in 2019 decreased by $4.3 million, or 16%, as compared to 2018. The decrease was primarily driven by a reduction in the number of global services and cloud infrastructure personnel, which led to a decrease of $2.2 million in compensation and benefits expense, including stock-based compensation expense, as compared to 2018. This reduction in headcount also contributed to a decrease in allocated facilities and information technology costs of $0.5 million in 2019. We also experienced a decrease of $0.9 million in hosting costs in 2019, due to a decline in the usage of our hosted platform as compared to 2018. Additionally, depreciation decreased $0.5 million in 2019, due to the nature and timing of capital expenditures and internal projects as compared to 2018.\nOur gross margin decreased to 53% during 2019, as compared to 54% during 2018. This was primarily due to our revenues, net declining during the year at a slightly faster rate than the corresponding decrease in costs.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "595d82dc74f20aabd4c6c3a7c7ac667e", "source_table_id": "434b94166389d203839e681526978a7d"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:b5d0a13b7a7903f6c513f0d6eca02fca", "source": "tatqa", "table_key": "f2f282e254aaea7042db5553ccb7ed5e73054689", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended August 31,", ""], ["", "2019", "2018", "2017"], ["Net cash provided by (used in) operating activities", "$1,193,066", "$(1,105,448)", "$(1,464,085)"], ["Net cash (used in) provided by investing activities", "(872,454)", "1,240,914", "2,141,263"], ["Net cash used in financing activities", "(415,772)", "(47,044)", "(404,546)"], ["Effect of exchange rate changes on cash and cash equivalents", "554", "(20,392)", "5,228"], ["Net (decrease) increase in cash and cash equivalents", "$(94,606)", "$68,030", "$277,860"]]}, "native_split": "test", "question": "What years does the table provide data for Net cash provided by (used in) operating activities?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018", "2017"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Cash Flows\nThe following table sets forth selected consolidated cash flow information (in thousands):\nOperating Activities Net cash provided by operating activities during the fiscal year ended August 31, 2019 was primarily due to increased accounts payable, accrued expenses and other liabilities, decreased inventories and non-cash expenses, partially offset by increased contract assets and accounts receivable. The increase in accounts payable, accrued expenses and other liabilities is primarily due to the timing of collections on accounts receivable sold under the securitization programs and the timing of purchases and cash payments. The decrease in inventories is primarily due to the adoption of ASU 2014-09 and the reclassification to contract assets for revenue recognized for over time customers, partially offset by an increase in inventories to support expected sales levels in the first quarter of fiscal year 2020. The increase in contract assets is due to the adoption of ASU 2014-09 and the timing of revenue recognition for over time customers. The increase in accounts receivable is primarily driven by the amended and new securitization programs and higher sales and timing of collections.\nInvesting Activities Net cash used in investing activities during the fiscal year ended August 31, 2019 consisted primarily of capital expenditures principally to support ongoing business in the DMS and EMS segments and expenditures for assets acquired in connection with the initial and second closings of the acquisition of certain assets of JJMD, partially offset by proceeds and advances from the sale of property, plant and equipment and cash receipts on sold receivables under the asset-backed securitization programs.\nFinancing Activities Net cash used in financing activities during the fiscal year ended August 31, 2019 was primarily due to: (i) payments for debt agreements, (ii) the repurchase of our common stock, (iii) dividend payments and (iv) treasury stock minimum tax withholding related to vesting of restricted stock. Net cash used in financing activities was partially offset by: (i) borrowings under debt agreements and (ii) net proceeds from the exercise of stock options and issuance of common stock under the employee stock purchase plan.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 5, "source_local_id": "b5d0a13b7a7903f6c513f0d6eca02fca", "source_table_id": "0b31ec7e0200c0354c067a2cf192f4af"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:c5e66bb5c3a45464a6a9437d1d3b4584", "source": "tatqa", "table_key": "33c6019109c11ad881d8286878ec6eecc532c802", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 3, "section_rows": [], "cells": [["Name", "Age", "Position"], ["Avi Katz", "61", "President, General Counsel and Secretary since December 2012. Senior Vice President, General Counsel and Secretary from January 2008 to December 2012."], ["John Capogrossi", "66", "Vice President, Chief Financial Officer and Treasurer since January 2016. Vice President, Chief Financial Officer, Treasurer and Controller from March 2013 to January 2016. Vice President and Controller from January 2008 to March 2013."], ["Ravinder S. Girgla", "56", "Vice President and Controller since January 2016. Deputy Controller from February 2013 to January 2016. Assistant Controller from July 2008 to February 2013."]]}, "native_split": "test", "question": "Where can the remaining information required under Item 10 concerning the executive officers of Loral as of March 12, 2020 be found?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["The Company’s 2020 definitive proxy statement", "Form 10‐K"], "answer_type": "multi-span", "answer_from": "text", "paragraphs": "Item 10. Directors, Executive Officers and Corporate Governance\nExecutive Officers of the Registrant\nThe following table sets forth information concerning the executive officers of Loral as of March 12, 2020.\nThe remaining information required under Item 10 will be presented in the Company’s 2020 definitive proxy statement which is incorporated herein by reference or by amendment to this Annual Report on Form 10‐K.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "c5e66bb5c3a45464a6a9437d1d3b4584", "source_table_id": "616265ea38390d64cc296291b9de3fbc"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:5daf2c91d3b8cacd5370d907d95f0e96", "source": "tatqa", "table_key": "5cc02fce0d5e31d7c0bbb452c9a3a45052990dd5", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 5, "section_rows": [], "cells": [["", "", "2019", "", "2018"], ["", "Gross Carrying Amount", "Accumulated Amortization", "Gross Carrying Amount", "Accumulated Amortization"], ["Non-amortizing intangible assets", "$3,678.0", "$—", "$918.3", "$—"], ["Amortizing intangible assets .", "1,244.2", "260.8", "576.6", "212.1"], ["", "$4,922.2", "$260.8", "$1,494.9", "$212.1"]]}, "native_split": "test", "question": "What was the accumulated amortization of fiscal years 2018 and 2019, respectively? ", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$212.1", "$260.8"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Other identifiable intangible assets were as follows:\nNon-amortizing intangible assets are comprised of brands and trademarks.\nAmortizing intangible assets, carrying a remaining weighted-average life of approximately 20 years, are principally composed of customer relationships, and acquired intellectual property. For fiscal 2019, 2018, and 2017, we recognized amortization expense of $49.1 million, $34.9 million, and $33.6 million, respectively. Based on amortizing assets recognized in our Consolidated Balance Sheet as of May 26, 2019, amortization expense is estimated to average $58.3 million for each of the next five years, with a high expense of $59.9 million in fiscal 2020 and decreasing to a low expense of $54.2 million in fiscal 2024.\nDuring fiscal 2019, in conjunction with the divestiture of our Italian-based frozen pasta business, Gelit, we reclassified $15.1 million and $1.7 million of goodwill and other identifiable intangible assets, respectively, to noncurrent assets held for sale for periods prior to the divestiture.\nDuring fiscal 2019, as a result of our annual impairment test for indefinite lived intangibles, we recognized impairment charges of $76.5 million for our Chef Boyardee® and Red Fork® brands in our Grocery & Snacks segment. We also recognized impairment charges of $13.1 million for our Aylmer® and Sundrop ® brands in our International segment.\nDuring fiscal 2018, as a result of our annual impairment test for indefinite lived intangibles, we recognized impairment charges of $4.0 million for our HK Anderson® , Red Fork® , and Salpica® brands in our Grocery & Snacks segment. We also recognized an impairment charge of $0.8 million for our Aylmer® brand in our International segment.\nDuring fiscal 2017, we recorded goodwill impairment charges in our International reporting segment totaling $198.9 million, of which $139.2 million related to our Canadian reporting unit and $59.7 million related to our Mexican reporting unit. These impairment charges resulted from a change in reporting segments, which occurred in the first quarter of fiscal 2017 when we were required to determine new reporting units at a lower level, and from further deterioration in forecasted sales and profits during fiscal 2017, which were caused primarily by changes in foreign exchange rates.\nIn fiscal 2017, due to declining sales of certain brands, we elected to perform a quantitative impairment test for indefinite lived intangibles of those brands. During fiscal 2017, we recognized impairment charges of $31.5 million for our Del Monte® brand and $5.5 million for our Aylmer® brand in our International segment. We also recognized impairment charges of $67.1 million for our Chef Boyardee® brand and $1.1 million for our Fiddle Faddle® brand in our Grocery & Snacks segment.\nNotes to Consolidated Financial Statements - (Continued) Fiscal Years Ended May 26, 2019, May 27, 2018, and May 28, 2017 (columnar dollars in millions except per share amounts)", "scale": "million", "scoring": "set_match", "meta": {"paragraph_count": 9, "source_local_id": "5daf2c91d3b8cacd5370d907d95f0e96", "source_table_id": "d61506b77b7d2ccadf89b45df639c0c3"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:6529d497a5be9783bd4ff0d48f3c2d1d", "source": "tatqa", "table_key": "925c542ba9e296e8c9e8815bcce06b6a9eaac0fc", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2018", "2019"], ["Personnel-related items", "32,636", "45,318"], ["Deferred revenue", "28,255", "32,146"], ["Financing related items", "–", "3,912"], ["Current portion of lease liabilities", "–", "7,002"], ["Advanced payments from customers", "25,382", "47,601"], ["Supplier related items", "1,828", "2,175"], ["Marketing related items", "1,049", "1,365"], ["R&D projects", "1,209", "1,109"], ["Other", "8,634", "9,215"], ["Total accrued expenses and other payables", "98,993", "149,843"]]}, "native_split": "test", "question": "What are the components of Accrued expenses and other payables?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["Personnel-related items", "Deferred revenue", "Financing related items", "Current portion of lease liabilities", "Advanced payments from customers", "Supplier related items", "Marketing related items", "R&D projects", "Other"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "NOTE 15. ACCRUED EXPENSES AND OTHER PAYABLES\nAccrued expenses and other payables consist of the following:\nPersonnel-related items comprise accrued management bonuses, accrued vacation days, accrued wage tax, social securities and pension premiums. Deferred revenue consists of the revenue relating to the undelivered elements of the arrangements, see Note 21 for more information. This part of revenue is deferred at their relative selling prices until delivery of these elements. Other includes accruals for VAT, other taxes and invoices to be received for goods and services.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "6529d497a5be9783bd4ff0d48f3c2d1d", "source_table_id": "9aa587f522dd1d5bd2c0e6638699f52e"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:880a5871d3cd2cf8ae27eb3454f2d710", "source": "tatqa", "table_key": "9148e94b0aa95fd12e0a853d3378c48073d2a186", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 5, "section_rows": [], "cells": [["", "", "December 31, 2018", "", ""], ["(in thousands)", "Amortized Cost", "Unrealized Gains", "Unrealized Losses", "Fair Value"], ["Municipal bonds", "$44,802", "$13", "$(110)", "$44,705"], ["Corporate bonds", "48,499", "23", "(226)", "48,296"], ["", "$93,301", "$36", "$(336)", "$93,001"]]}, "native_split": "test", "question": "What are the respective values of the company's unrealized gains from its municipal and corporate bonds?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$13", "23"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "3. MARKETABLE SECURITIES\nAs of December 31, 2019, the Company did not hold any marketable securities.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "880a5871d3cd2cf8ae27eb3454f2d710", "source_table_id": "d1b5b0723a3c83bff8f3640c9689e9a8"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:0b268aa13efe3e52409849e3cd1b711c", "source": "tatqa", "table_key": "d1cc99d59db31b3348170d4bb477224d79dfa42b", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 5, "section_rows": [], "cells": [["", "", "", "Issue price", ""], ["Details", "Date", "Shares", "AU$US", "$’000"], ["Balance", "1 July 2017", "130,215,813", "", "125,177"], ["Shares issued", "23 February 2018", "69,129", "$8.52", "458"], ["Balance", "30 June 2018", "130,284,942", "", "125,635"], ["Shares issued", "24 August 2018", "69,129", "$8.46", "423"], ["Share issued", "24 August 2018", "28,407", "$0.00", "-"], ["Share issued", "21 December 2018", "129,044", "$0.00", "-"], ["Balance", "30 June 2019", "130,511,522", "", "126,058"]]}, "native_split": "test", "question": "What are the dates that shares were issued?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["23 February 2018", "24 August 2018", "21 December 2018"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Movements in ordinary share capital\nOrdinary Shares\nOrdinary shares entitle the holder to participate in dividends and the proceeds on the winding up of the company in proportion to the number of and amounts paid on the shares held. The fully paid ordinary shares have no par value and the company does not have a limited amount of authorised capital.\nOn a show of hands every member present at a meeting in person or by proxy shall have one vote and upon a poll each share shall have one vote.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 4, "source_local_id": "0b268aa13efe3e52409849e3cd1b711c", "source_table_id": "e83a8720c4e56cff98694396c55e9137"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:bb136afdcf735df09b34e1bec306ab17", "source": "tatqa", "table_key": "4a614b8fc31cb1b907989cc8d38c4d32da5100a7", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 5, "section_rows": [], "cells": [["", "Group", "", "Company", ""], ["", "2019", "2018", "2019", "2018"], ["", "£m", "£m", "£m", "£m"], ["Share Incentive Plan (‘SIP’)", "–", "0.8", "–", "–"], ["Sharesave scheme (‘SAYE’)", "0.3", "0.3", "–", "–"], ["Performance Share Plan (‘PSP’)", "2.1", "1.8", "1.3", "0.7"], ["Deferred Annual Bonus and Single Incentive Plan", "2.3", "0.4", "0.4", "0.2"], ["Total share-based payment charge", "4.7", "3.3", "1.7", "0.9"], ["NI and apprenticeship levy on applicable schemes", "1.2", "0.4", "0.6", "0.1"], ["Total charge", "5.9", "3.7", "2.3", "1.0"]]}, "native_split": "test", "question": "In which years were the share-based payments for the Share Incentive Plan ('SIP') accounted for?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "29. Share-based payments continued\nShare Incentive Plan\nIn 2015, the Group established a Share Incentive Plan (‘SIP’). All eligible employees were awarded free shares (or nil-cost options in the case of employees in Ireland) valued at £3,600 each based on the share price at the time of the Company’s admission to the Stock Exchange in March 2015, subject to a three-year service period (‘Vesting Period’). The SIP shareholders are entitled to dividends over the Vesting Period. There are no performance conditions applicable to the vesting of SIP shares. The fair value of the SIP awards at the grant date was measured to be £2.72 using the Black-Scholes model. The resulting share-based payments charge is being spread evenly over the Vesting Period.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "bb136afdcf735df09b34e1bec306ab17", "source_table_id": "718c2f0e3e14824e3366330fd18eb128"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:d1d3ffbba916f628660f222fbf0a6505", "source": "tatqa", "table_key": "5dbd6eef3ba8ed70416ba12a295f0bd45a83a4f5", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31,2018"], ["Accounts receivable", "$68,309", "$76,376"], ["Less: provision for doubtful receivables", "(16,377)", "(16,086)"], ["Accounts receivable, net", "$51,932", "$60,290"]]}, "native_split": "test", "question": "Which years does the table provide information for accounts receivable?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nNOTE 5: ACCOUNTS RECEIVABLE, NET\nAccounts receivable consisted of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "d1d3ffbba916f628660f222fbf0a6505", "source_table_id": "5208576a85192612b24f629769981aa8"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:5df9d6b9c0dda5ed0517d87d86dc6d0b", "source": "tatqa", "table_key": "937329e9dcc81e923d333dd684306c88dfd3c5b4", "grid": {"TD": 1, "LD": 0, "nrows": 24, "ncols": 4, "section_rows": [], "cells": [["", "", "Years ended December 31,", ""], ["", "2019", "2018", "2017"], ["", "(in thousands, except share and per share data)", "", ""], ["Revenues:", "", "", ""], ["Subscription solutions", "$642,241", "$464,996", "$310,031"], ["Merchant solutions", "935,932", "608,233", "363,273"], ["", "1,578,173", "1,073,229", "673,304"], ["Cost of revenues(1)(2):", "", "", ""], ["Subscription solutions", "128,155", "100,990", "61,267"], ["Merchant solutions", "584,375", "375,972", "231,784"], ["", "712,530", "476,962", "293,051"], ["Gross profit", "865,643", "596,267", "380,253"], ["Operating expenses:", "", "", ""], ["Sales and marketing(1)(2)", "472,841", "350,069", "225,694"], ["Research and development(1)(2)", "355,015", "230,674", "135,997"], ["General and administrative(1)", "178,934", "107,444", "67,719"], ["Total operating expenses", "1,006,790", "688,187", "429,410"], ["Loss from operations", "(141,147)", "(91,920)", "(49,157)"], ["Other income", "45,332", "27,367", "9,162"], ["Loss before income taxes", "(95,815)", "(64,553)", "(39,995)"], ["Provision for income taxes", "29,027", "—", "—"], ["Net loss", "$(124,842)", "$(64,553)", "$(39,995)"], ["Basic and diluted net loss per share attributable to shareholders", "$(1.10)", "$(0.61)", "$(0.42)"], ["Weighted average shares used to compute net loss per share attributable to shareholders", "113,026,424", "105,671,839", "95,774,897"]]}, "native_split": "test", "question": "Which financial years' information is shown in the table?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2017", "2018", "2019"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "Results of Operations\nThe following table sets forth our consolidated statement of operations for the years ended December 31, 2019, 2018, and 2017.\n(1) Includes stock-based compensation expense and related payroll taxes as follows:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "5df9d6b9c0dda5ed0517d87d86dc6d0b", "source_table_id": "1af49cc81138fdb7e0edcaf74ad5d65e"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:ed9383626f02af2c40e43287a2bcae8d", "source": "tatqa", "table_key": "c52c2ed77e979c404e3e63cd21611733dac874da", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Shares of common stock issuable under equity incentive plans outstanding", "6,832", "8,943", "10,806"], ["Convertible senior notes", "1,905", "79", "-"], ["Potential common shares excluded from diluted net loss per share", "8,737", "9,022", "10,806"]]}, "native_split": "test", "question": "What are the respective number of convertible senior notes in 2018 and 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["79", "1,905"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "The following table summarizes the potentially dilutive common shares that were excluded from diluted weighted-average common shares outstanding because including them would have had an anti-dilutive effect (in thousands):\nSince the Company expects to settle the principal amount of its outstanding convertible senior notes in cash and any excess in cash or shares of the Company’s Class A Common Stock, the Company uses the treasury stock method for calculating any potential dilutive effect of the conversion spread on diluted net income per share, if applicable. The conversion spread will have a dilutive impact on diluted net income per share of common stock when the average market price of the Company’s Class A Common Stock for a given period exceeds the conversion price of $81.45 per share for the Notes.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "ed9383626f02af2c40e43287a2bcae8d", "source_table_id": "8a593a14363893b7570489580a9d0545"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:ac543087b7cc0bc8ab7cc531f117e33f", "source": "tatqa", "table_key": "930f68df67b427f750a1a30b461a0386268e6acc", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 4, "section_rows": [], "cells": [["", "Year Ended December 31,", "", ""], ["", "2018", "2017", "Change"], ["Other Operating Data", "", "", ""], ["Average Revenue Per Unit (ARPU)", "", "", ""], ["ARPU—on-net", "$ 480", "$ 506", "(5.1)%"], ["ARPU—off-net", "$ 1,155", "$ 1,239", "(6.8)%"], ["Average price per megabit", "$ 0.82", "$ 1.11", "(25.9)%"], ["Customer Connections—end of period", "", "", ""], ["On-net", "68,770", "61,334", "12.1%"], ["Off-net", "10,974", "9,953", "10.3%"]]}, "native_split": "test", "question": "What are the respective APRU from on-net in 2017 and 2018 respectively?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["$ 506", "$ 480"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Year Ended December 31, 2018 Compared to the Year Ended December 31, 2017\nOur management reviews and analyzes several key financial measures in order to manage our business and assess the quality of and variability of our service revenue, operating results and cash flows. The following summary tables present a comparison of our results of operations with respect to certain key financial measures. The comparisons illustrated in the tables are discussed in greater detail below.", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 2, "source_local_id": "ac543087b7cc0bc8ab7cc531f117e33f", "source_table_id": "2f7d749e5b10203f268b5c1ef8f54a6b"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:84540a139de762e14b31a9e455234b7d", "source": "tatqa", "table_key": "92b21e2a6aaa43a35bcc0fbdff7138ed113f1bbf", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Land and land improvements", "$1,095", "$1,136"], ["Buildings and improvements", "68,350", "70,522"], ["Machinery and equipment", "224,312", "231,619"], ["Less: Accumulated depreciation", "(188,719)", "(203,876)"], ["Property, plant and equipment, net", "$105,038", "$99,401"]]}, "native_split": "test", "question": "Which years does the table provide information for the company's Property, plant and equipment?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["2019", "2018"], "answer_type": "multi-span", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nNOTE 5 — Property, Plant and Equipment\nProperty, plant and equipment is comprised of the following:", "scale": "", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "84540a139de762e14b31a9e455234b7d", "source_table_id": "903322d78b47417847ca16d46feb6653"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:d12f54fe739eb51fa0bb1a08961ff669", "source": "tatqa", "table_key": "9b70a47eedf99e9dafe414609467e26a7a2a818b", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "Year Ended December 31,", "", "% Change"], ["", "2019", "2018", "2019"], ["", "", "(dollars in thousands)", ""], ["Cost of net revenue", "$149,495", "$176,223", "(15)%"], ["% of net revenue", "47%", "46%", ""], ["Gross profit", "167,685", "208,774", "(20)%"], ["% of net revenue", "53%", "54%", ""]]}, "native_split": "test", "question": "What was the respective gross profit in 2019 and 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": ["167,685", "208,774"], "answer_type": "multi-span", "answer_from": "table", "paragraphs": "Cost of Net Revenue and Gross Profit\nCost of net revenue decreased $26.7 million to $149.5 million for the year ended December 31, 2019, as compared to $176.2 million for the year ended December 31, 2018. The decrease was primarily driven by lower sales. The decrease in gross profit percentage for the year ended December 31, 2019, as compared to the year ended December 31, 2018, was due to lower revenue and product mix.\nWe currently expect that gross profit percentage will fluctuate in the future, from period-to-period, based on changes in product mix, average selling prices, and average manufacturing costs.", "scale": "thousand", "scoring": "set_match", "meta": {"paragraph_count": 3, "source_local_id": "d12f54fe739eb51fa0bb1a08961ff669", "source_table_id": "f377d3e49270744c5c7e960acdcef404"}, "dataset": "tatqa", "subtype": "multi-span"}
{"id": "tatqa:3043971ee8b1d77cdbebedeec092a2d4", "source": "tatqa", "table_key": "bdfe044f4f68ad20c596ee7f52a3bdbf954b0189", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31, 2018"], ["Principal amount", "$300,000", "$300,000"], ["Unamortized debt discount", "(2,691)", "(4,348)"], ["Net carrying amount before unamortized debt issuance costs", "297,309", "295,652"], ["Unamortized debt issuance costs", "(4,135)", "(6,685)"], ["Net carrying value", "$293,174", "$288,967"]]}, "native_split": "test", "question": "What was the principal amount in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$300,000", "answer_type": "span", "answer_from": "table-text", "paragraphs": "9. DEBT AND OTHER FINANCING ARRANGEMENTS\n2021 Senior Convertible Notes\nIn 2017, the Company issued $300.0 million principal amount of 5.75% senior convertible notes (the “2021 Notes”) for a purchase price equal to 98% of the principal amount. The Company received net proceeds of $284.9 million, net of a discount of $6.0 million and issuance costs of $9.1 million. The debt discount is being accreted to interest expense over the term of the 2021 Notes using the interest method. The issuance costs were deferred and are being amortized to interest expense over the same term.\nThe 2021 Notes are governed by an Indenture, dated December 8, 2017 between the Company and US Bank National Association, as trustee (the “2017 Indenture”). The 2021 Notes mature on July 1, 2021, unless earlier repurchased or converted. Interest is payable semi-annually in arrears on January 1 and July 1, commencing January 1, 2018.\nThe 2021 Notes are convertible at an initial conversion rate of 23.8095 shares of the Company’s common stock per $1,000 principal amount of the 2021 Notes, which represents an initial conversion price of $42.00 per share, subject to adjustment for anti-dilutive issuances, voluntary increases in the conversion rate, and make-whole adjustments upon a fundamental change. A fundamental change includes a change in control, delisting of the Company’s common stock, and a liquidation of the Company. Upon conversion, the Company will deliver the applicable number of the Company’s common stock and cash in lieu of any fractional shares. Holders of the 2021 Notes may convert their 2021 Notes at any time prior to the close of business on the scheduled trading day immediately preceding the maturity date.\nThe holders of the 2021 Notes may require the Company to repurchase all or a portion of their 2021 Notes at a cash repurchase price equal to 100% of the principal amount of the 2021 Notes being repurchased, plus the remaining scheduled interest through and including the maturity date, upon a fundamental change and events of default, including non-payment of interest or principal and other obligations under the 2017 Indenture.\nThe net carrying amounts of the liability components of the 2021 Notes consist of the following (in thousands):\nThe effective interest rate of the liability component is 6.4% for the 2021 Notes.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 8, "source_local_id": "3043971ee8b1d77cdbebedeec092a2d4", "source_table_id": "3db9dd3c463d65c10aac1bbfe899990b"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:12cf3a1c59b97362f9d3a270158e7d13", "source": "tatqa", "table_key": "711df6e43d5ecda5a42d8ef8cbc8e2f287af788b", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Cash and cash equivalents", "$1,501.2", "$1,208.7", "$802.1"], ["Restricted cash", "76.8", "96.2", "152.8"], ["Total cash, cash equivalents and restricted cash", "$1,578.0", "$1,304.9", "$954.9"]]}, "native_split": "test", "question": "What was the Restricted cash in 2017?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "152.8", "answer_type": "span", "answer_from": "table-text", "paragraphs": "AMERICAN TOWER CORPORATION AND SUBSIDIARIES NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (Tabular amounts in millions, unless otherwise disclosed)\nCash and Cash Equivalents—Cash and cash equivalents include cash on hand, demand deposits and short-term investments with original maturities of three months or less. The Company maintains its deposits at high-quality financial institutions and monitors the credit ratings of those institutions.\nRestricted Cash—Restricted cash includes cash pledged as collateral to secure obligations and all cash whose use is otherwise limited by contractual provisions.\nThe reconciliation of cash and cash equivalents and restricted cash reported within the applicable balance sheet that sum to the total of the same such amounts shown in the statements of cash flows is as follows:", "scale": "million", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "12cf3a1c59b97362f9d3a270158e7d13", "source_table_id": "293279f81a05c9d75adf2cc2ef06ec35"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:c447c5ff9ca6cfb0b23f273af3ac9a27", "source": "tatqa", "table_key": "e6f3c29d38535c9961016b049efa96c395f351ca", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 4, "section_rows": [], "cells": [["Years ended August 31,", "2019", "2018 (1)", "Change"], ["(in thousands of dollars, except percentages)", "$", "$", "%"], ["Cash flow from operating activities", "868,711", "620,748", "39.9"], ["Cash flow from investing activities", "(471,078)", "(2,191,666)", "(78.5)"], ["Cash flow from financing activities", "(659,222)", "1,426,136", "—"], ["Effect of exchange rate changes on cash and cash equivalents denominated in a foreign currency", "(439)", "1,989", "—"], ["Net change in cash and cash equivalents from continuing operations", "(262,028)", "(142,793)", "83.5"], ["Net change in cash and cash equivalent from discontinued operations(2)", "733,807", "16,333", "—"], ["Cash and cash equivalents, beginning of the year", "84,725", "211,185", "(59.9)"], ["Cash and cash equivalents, end of the year", "556,504", "84,725", "—"]]}, "native_split": "test", "question": "What was the percentage change in Cash and cash equivalents, beginning of the year from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "(59.9)", "answer_type": "span", "answer_from": "table", "paragraphs": "5. CASH FLOW ANALYSIS\n(1) Fiscal 2018 was restated to comply with IFRS 15 and to reflect a change in accounting policy as well as to reclassify results from Cogeco Peer 1 as discontinued operations. For further details, please consult the \"Accounting policies\" and \"Discontinued operations\" sections.\n(2) For further details on the Corporation's cash flow attributable to discontinued operations, please consult the \"Discontinued operations\" section.\nFiscal 2019 cash flow from operating activities increased by 39.9% compared to the prior year mainly from: • higher adjusted EBITDA; • the decreases in income taxes paid and in financial expense paid; and • the decrease in integration, restructuring and acquisitions costs.\nFiscal 2019 investing activities decreased by 78.5% compared to the prior year mainly due to the MetroCast acquisition of $1.76 billion in the second quarter of fiscal 2018.", "scale": "percent", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "c447c5ff9ca6cfb0b23f273af3ac9a27", "source_table_id": "a533cbf8b477a41e6b83ce4c69b67b85"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:7cb0ad318722820690b52bfdac79a1c0", "source": "tatqa", "table_key": "58f53ecbcef7fc7d44b89b960d63af42fad1c9ed", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["€ million", "30/9/2018", "30/9/2019"], ["Provisions for post-employment benefits plans (employer’s commitments)", "344", "414"], ["Provisions for indirect commitments", "12", "17"], ["Provisions for voluntary pension benefits", "0", "0"], ["Provisions for post-employment benefit plans", "71", "78"], ["", "427", "509"], ["Provisions for obligations similar to pensions", "41", "34"], ["", "468", "543"]]}, "native_split": "test", "question": "How are Provisions for post-employment benefits plans recognised?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "in accordance with IAS 19 (Employee Benefits)", "answer_type": "span", "answer_from": "table-text", "paragraphs": "32. Provisions for post-employment benefits plans and similar obligations\nProvisions for post-employment benefits plans are recognised in accordance with IAS 19 (Employee Benefits).\nProvisions for post-employment benefits plans consist of commitments primarily related to benefits defined by the provisions of company pension plans. These take the form of defined benefit plans directly from the employer (employer’s commitments) and defined benefit plans from external pension providers (benevolent funds in Germany and international pension funds). The external providers’ assets serve exclusively to finance the pension entitlements and qualify as plan assets. The benefits under the different plans are based on performance and length of service.\nThe most important performance-based pension plans are described in the following.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "7cb0ad318722820690b52bfdac79a1c0", "source_table_id": "832f8af1730c8f79e9d70fc20c3e9ed5"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:24e82909bdb620394f476d0cf626b477", "source": "tatqa", "table_key": "595a09e7fa4032b93914b781bb71288d7a3a32e7", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "2019", "2018", "2017"], ["Risk-free interest rate", "2.68%", "1.74%-1.94%", "0.94%-2.14%"], ["Expected life (in years)", "5", "5", "5"], ["Expected volatility", "32.42%", "32.42% - 32.84%", "35.25%-40.22%"], ["Weighted average grant date fair value", "$4.72", "$3.36", "$3.69"]]}, "native_split": "test", "question": "What was the risk free interest rate in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2.68%", "answer_type": "span", "answer_from": "table", "paragraphs": "Stock-Settled Stock Appreciation Rights\nStock-Settled Appreciation Rights (“SSARs”) are rights granted to an employee to receive value equal to the difference in the price of our common shares on the date of the grant and on the date of exercise. This value is settled only in common shares of Agilysys.\nWe use a Black-Scholes-Merton option pricing model to estimate the fair value of SSARs. The following table summarizes the principal assumptions utilized in valuing SSARs granted in fiscal 2019, 2018 and 2017:\nThe risk-free interest rate is based on the yield of a zero coupon U.S. Treasury bond whose maturity period approximates the expected life of the SSARs. The expected life is estimated using historical data representing the period of time the awards are expected to be outstanding. The estimated fair value of the SSARs granted is recognized over the vesting period of the awards utilizing the graded vesting method. Under this method, the compensation cost related to unvested amounts begins to be recognized as of the grant date.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "24e82909bdb620394f476d0cf626b477", "source_table_id": "6bd0f8a7f2b32dbe8e6563877b4663de"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:7be50a024f33fd10128205b582139f93", "source": "tatqa", "table_key": "e46d82e19d87d10d71ba847105d54c41f7744d03", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "Target Allocations", "Percentage of Plan Assets at December 31,", ""], ["Asset Category", "2020", "2019", "2018"], ["Equity securities", "13%", "13%", "12%"], ["Debt securities", "83%", "83%", "84%"], ["Other", "4%", "4%", "4%"], ["Total", "100%", "100%", "100%"]]}, "native_split": "test", "question": "What was the percentage of Plan Assets for Other assets in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "4", "answer_type": "span", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nOur pension plan asset allocation at December 31, 2019, and 2018, and target allocation for 2020 by asset category are as follows:\nWe employ a liability-driven investment strategy whereby a mix of equity and fixed-income investments are used to pursue a derisking strategy which over time seeks to reduce interest rate mismatch risk and other risks while achieving a return that matches or exceeds the growth in projected pension plan liabilities. Risk tolerance is established through careful consideration of plan liabilities and funded status. The investment portfolio primarily contains a diversified mix of equity and fixed-income investments. Other assets such as private equity are used modestly to enhance long-term returns while improving portfolio diversification. Investment risk is measured and monitored on an ongoing basis through quarterly investment portfolio reviews, annual liability measurements, and asset/liability studies at regular intervals.", "scale": "percent", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "7be50a024f33fd10128205b582139f93", "source_table_id": "054153aec5a8b7066b1083f3ec3515ed"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:85239559504b80773492989196b047d2", "source": "tatqa", "table_key": "d16e83783613e135838eb8c0d6fc504998c4674d", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["", "2019", "2018", "CHANGE", "% CHANGE"], ["Retail net subscriber activations (losses) (1)", "6,053", "21,559", "(15,506)", "(71.9%)"], ["IPTV", "91,476", "110,790", "(19,314)", "(17.4%)"], ["Satellite", "(85,423)", "(89,231)", "3,808", "4.3%"], ["Total retail subscribers (1)", "2,772,464", "2,766,411", "6,053", "0.2%"], ["IPTV", "1,767,182", "1,675,706", "91,476", "5.5%"], ["Satellite", "1,005,282", "1,090,705", "(85,423)", "(7.8%)"]]}, "native_split": "test", "question": "What was the change in the total retail subscribers?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "6,053", "answer_type": "span", "answer_from": "table", "paragraphs": "Retail TV\n(1) As of January 1, 2019, we are no longer reporting wholesale subscribers in our TV subscriber base reflecting our focus on the retail market. Consequently, we restated previously reported 2018 subscribers for comparability.\nRetail IPTV net subscriber activations decreased by 17.4% in 2019, compared to last year, resulting from the impact of a maturing Fibe TV market, slower new service footprint growth and greater substitution of traditional TV services with OTT services, partly offset by higher Alt TV activations.\nRetail satellite TV net customer losses improved by 4.3% compared to 2018, attributable to lower deactivations, reflecting a more mature subscriber base geographically better-suited for satellite TV service.\nTotal retail TV net subscriber activations (IPTV and satellite TV combined) decreased by 71.9% in 2019, compared to last year, due to lower IPTV net activations, moderated by fewer satellite TV net losses.\nRetail IPTV subscribers at December 31, 2019 totaled 1,767,182, up 5.5% from 1,675,706 subscribers reported at the end of 2018.\nRetail satellite TV subscribers at December 31, 2019 totaled 1,005,282, down 7.8% from 1,090,705 subscribers at the end of last year.\nTotal retail TV subscribers (IPTV and satellite TV combined) at December 31, 2019 were 2,772,464, representing a 0.2% increase since the end of 2018.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 8, "source_local_id": "85239559504b80773492989196b047d2", "source_table_id": "a8c11cade3237853c6a6869143f7ec37"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:0a7767ec26c3d679e7e1cb78b9aa155a", "source": "tatqa", "table_key": "5fa28bbf21f14a439a7268b41a3bd06240650f17", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["(in thousands of $)", "2019", "2018"], ["Oil derivative instrument (see note 24)", "45,640", "84,730"], ["Operating lease right-of-use-assets (1)", "9,847", "—"], ["Foreign exchange swap (see note 24)", "214", "—"], ["Mark-to-market interest rate swaps valuation (see note 24)", "8", "6,298"], ["Investment in OLT-O (2)", "—", "7,347"], ["Other non-current assets (3)", "24,700", "40,729"], ["", "80,409", "139,104"]]}, "native_split": "test", "question": "In which year was the oil derivative instrument higher?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2018", "answer_type": "span", "answer_from": "table", "paragraphs": "17. OTHER NON-CURRENT ASSETS\n(1) Following the adoption of ASC 842, the balance sheet presents right-of-use-assets which mainly comprise of our office leases. This standard has been adopted under a modified retrospective transition approach as of January 1, 2019.\n(2) Investment in OLT-O refers to our investment in an Italian incorporated unlisted company which is involved in the construction, development, operation and maintenance of a FSRU terminal to be situated off the Livorno coast of Italy, representing a 2.7% interest in OLT-O’s issued share capital. In May 2019, a major shareholder sold its shareholding which triggered a re-assessment of the carrying value of our investment in OLT-O. This resulted in an impairment charge of $7.3 million for the write down of the carrying value in our investment in OLT-O in the year ended December 31, 2019.\n(3) \"Other non-current assets\" as of December 31, 2019 includes payments made for long lead items ordered in preparation for the conversion of the Viking into an FSRU. As of December 31, 2019 the aggregate carrying value of Viking long lead items was $16.2 million.\n\"Other non-current assets\" as of December 31, 2018 was mainly comprised of payments made relating to long lead items ordered in preparation for the conversion of the Gimi into a FLNG vessel. Subsequent to the receipt of a Limited Notice to Proceed from BP in relation to the Greater Tortue Ahmeyim project in December 31, 2018, initial works of the FLNG conversion commenced in January 2019. Consequently, as of December 31, 2019, the aggregate carrying value of $31.0 million has been reclassified to \"Asset under development\" (see note 15).", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "0a7767ec26c3d679e7e1cb78b9aa155a", "source_table_id": "de3a2139d0b87ed8ed23035ab0d73609"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:f22feee8205c1f0c24fbf429d2d8a20e", "source": "tatqa", "table_key": "437bb9d3f25eba682d21f1393411adee32e04fc3", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "Number of Shares", "Weighted-Average Grant\nDate Fair Value"], ["Nonvested shares of RSUs at December 31, 2018", "8.6", "$16.59"], ["Granted", "5.4", "21.64"], ["Achieved", "0.2", "24.46"], ["Released", "(4.8)", "14.41"], ["Canceled", "(0.5)", "19.74"], ["Nonvested shares of RSUs at December 31, 2019", "8.9", "20.84"]]}, "native_split": "test", "question": "How much was the Total compensation expense related to performance-based, service-based, and market-based RSUs for the year ended December 31, 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$69.8 million", "answer_type": "span", "answer_from": "text", "paragraphs": "Restricted Stock Units\nA summary of the RSU transactions for the year ended December 31, 2019 are as follows (number of shares in millions):\nDuring 2019, the Company awarded 2.6 million RSUs to certain officers and employees of the Company that vest upon the achievement of certain performance criteria and market conditions. The number of units expected to vest is evaluated each reporting period and compensation expense is recognized for those units for which achievement of the performance criteria is considered probable. Compensation expense for RSUs with market conditions are recognized based on the grant date fair value irrespective of the achievement of the condition.\nAs of December 31, 2019, unrecognized compensation expense, net of estimated forfeitures related to non-vested RSUs granted under the Amended and Restated SIP with service, performance and market conditions, was $60.9 million, $10.1 million and $3.9 million, respectively.\nFor RSUs with time-based service conditions, expense is being recognized over the vesting period; for RSUs with performance criteria, expense is recognized over the period during which the performance criteria is expected to be achieved; for RSUs with market conditions expense is recognized over the period in which the condition is assessed irrespective of whether it would be achieved or not.\nUnrecognized compensation cost related to awards with certain performance criteria that are not expected to be achieved is not included here. Total compensation expense related to performance-based, service-based, and market-based RSUs was $69.8 million for the year ended December 31, 2019, which included $48.4 million for RSUs with time-based service conditions that were granted in 2019 and prior that are expected to vest.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 6, "source_local_id": "f22feee8205c1f0c24fbf429d2d8a20e", "source_table_id": "8bf3df18a086f04608ee6da57046af30"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:68b509547a772b1dc40731f53bea1be8", "source": "tatqa", "table_key": "0e32328b0bc72f3aa68f0028902a31072e89e65f", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 7, "section_rows": [], "cells": [["", "", "", "Payments due by period", "", "", ""], ["(in thousands)", "2020", "2021 - 2022", "2023 - 2024", "2025 and thereafter", "Other", "Total"], ["Operating lease obligations (1)", "19,373", "36,373", "19,683", "1,666", "-", "$77,095"], ["Purchase obligations (2)", "$24,800", "$8,129", "$438", "$ -", "$ -", "$33,367"], ["Liability for uncertain tax positions (3)", "-", "-", "-", "-", "5,386", "$5,386"], ["Investment commitments (4)", "1,754", "205", "-", "-", "-", "$1,959"], ["Total", "$45,927", "$44,707", "$20,121", "$1,666", "$5,386", "$117,807"]]}, "native_split": "test", "question": "What does the company's liability for uncertain tax positions refer to?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "unable to reasonably estimate the timing of the cash outflow due to uncertainties in the timing of the effective settlement of tax positions.", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Contractual obligations\nAs of December 31, 2019, our contractual obligations were:\n(1) See \"9. Leases\" in Item 8 of this Annual Report for additional information.\n(2) Represents the fixed or minimum amounts due under purchase obligations for hosting services and sales and marketing programs\n(3) We are unable to reasonably estimate the timing of the cash outflow due to uncertainties in the timing of the effective settlement of tax positions.\n(4) Represents the maximum funding that would be expected under existing investment agreements with privately-held companies. Our investment agreements generally allow us to withhold unpaid committed funds at our discretion.\nA detailed discussion and analysis of the fiscal year 2017 year-over-year changes can be found in Item 7. Management’s Discussion and Analysis of Financial Condition and Results of Operations of our Annual Report on Form 10-K for the year ended December 31, 2018.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 7, "source_local_id": "68b509547a772b1dc40731f53bea1be8", "source_table_id": "38dfbf07be9b416017111086bdd21d57"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:cd28daf1b5dafb877634c4bcb1c468bd", "source": "tatqa", "table_key": "fc96067374f0d08be5b23dbe671abcfb258a9576", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 3, "section_rows": [], "cells": [["", "Fiscal Year", ""], ["($ in millions)", "2019", "2018"], ["Net sales", "$2,380.2", "$2,157.7"], ["Less: surcharge revenue", "438.1", "365.4"], ["Net sales excluding surcharge revenue", "$1,942.1", "$1,792.3"], ["Gross profit", "$444.8", "$382.3"], ["Gross margin", "18.7%", "17.7%"], ["Gross margin excluding surcharge revenue", "22.9%", "21.3%"]]}, "native_split": "test", "question": "How is the surcharge mechanism structured?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "to recover increases in raw material costs, although in certain cases with a lag effect as discussed above. While the surcharge generally protects the absolute gross profit dollars, it does have a dilutive effect on gross margin as a percent of sales.", "answer_type": "span", "answer_from": "text", "paragraphs": "Gross Profit\nGross profit in fiscal year 2019 increased to $444.8 million, or 18.7 percent of net sales from $382.3 million, or 17.7 percent of net sales for fiscal year 2018. Excluding the impact of the surcharge revenue, our gross margin in fiscal year 2019 was 22.9 percent compared to 21.3 percent in fiscal year 2018. The results reflect the impact of improved product mix coupled with capacity gains and operating cost reductions compared to the same period a year ago. Fiscal year 2019 also reflects an $11.4 million benefit related to an insurance recovery in our third fiscal quarter.\nOur surcharge mechanism is structured to recover increases in raw material costs, although in certain cases with a lag effect as discussed above. While the surcharge generally protects the absolute gross profit dollars, it does have a dilutive effect on gross margin as a percent of sales. The following represents a summary of the dilutive impact of the surcharge on gross margin. We present and discuss these financial measures because management believes removing the impact of surcharge provides a more consistent and meaningful basis for comparing results of operations from period to period. See the section “Non-GAAP Financial Measures” below for further discussion of these financial measures.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "cd28daf1b5dafb877634c4bcb1c468bd", "source_table_id": "b8cf8e9ebf71c892eda0a3ed9ee57852"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:660c91e9fab1746851bf84a1ba856cd9", "source": "tatqa", "table_key": "38395b60e8c2c5fafd1dd32b04a8f54f0a83a0da", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["2016 Facility", "$         —", "$   865.0"], ["$600   3.000% senior notes due 2020", "600.0", "600.0"], ["$500   2.800% senior notes due 2021", "500.0", "500.0"], ["$500   3.125% senior notes due 2022", "500.0", "500.0"], ["$700   3.650% senior notes due 2023", "700.0", "700.0"], ["$500   2.350% senior notes due 2024", "500.0", "—"], ["$300   3.850% senior notes due 2025", "300.0", "300.0"], ["$700   3.800% senior notes due 2026", "700.0", "700.0"], ["$800   4.200% senior notes due 2028", "800.0", "800.0"], ["$700   2.950% senior notes due 2029", "700.0", "—"], ["Other", "7.7", "3.0"], ["Less unamortized debt issuance costs", "(32.4)", "(26.3)"], ["Total debt", "5,275.3", "4,941.7"], ["Less current portion", "602.2", "1.5"], ["Long-term debt", "$ 4,673.1", "$ 4,940.2"]]}, "native_split": "test", "question": "What are the Company’s senior notes?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "unsecured senior obligations of the Company and rank equally in right of payment with all of Roper’s existing and future unsecured and unsubordinated indebtedness", "answer_type": "span", "answer_from": "text", "paragraphs": "(8) LONG-TERM DEBT\nOn September 23, 2016, Roper entered into a five-year $2.5 billion unsecured credit facility, as amended December 2, 2016, (the “2016 Facility”) with JPMorgan Chase Bank, N.A., as administrative agent, and a syndicate of lenders, which replaced its previous $1.85 billion unsecured credit facility dated as of July 27, 2012, as amended as of October 28, 2015 (the “2012 Facility”). The 2016 Facility comprises a five year $2.5 billion revolving credit facility, which includes availability of up to $150.0 for letters of credit. Roper may also, subject to compliance with specified conditions, request term loans or additional revolving credit commitments in an aggregate amount not to exceed $500.0. At December 31, 2019, there were $0.0 of outstanding borrowings under the 2016 Facility.\nThe 2016 Facility contains affirmative and negative covenants which, among other things, limit Roper’s ability to incur new debt, enter into certain mergers and acquisitions, sell assets and grant liens, make restricted payments (including the payment of dividends on our common stock) and capital expenditures, or change its line of business. Roper is also subject to financial covenants which require the Company to limit its consolidated total leverage ratio and to maintain a consolidated interest coverage ratio. The most restrictive covenant is the consolidated total leverage ratio which is limited to 3.50 to 1.\nThe 2016 Facility provides that the consolidated total leverage ratio may be increased, no more than twice during the term of the 2016 Facility, to 4.00 to 1 for a consecutive four quarter fiscal period per increase (or, for any portion of such four quarter fiscal period in which the maximum would be 4.25 to 1). In conjunction with the Deltek acquisition in December of 2016, we increased the maximum consolidated total leverage ratio covenant to 4.25 to 1 through June 30, 2017 and 4.00 to 1 through December 31, 2017.\nThe Company was in compliance with its debt covenants throughout the years ended December 31, 2019 and 2018.\nOn August 26, 2019, the Company completed a public offering of $500.0 aggregate principal amount of 2.35% senior unsecured notes due September 15, 2024 and $700.0 aggregate principal amount of 2.95% senior unsecured notes due September 15, 2029 (the “2019 Offering”). The notes bear interest at a fixed rate and are payable semi-annually in arrears on March 15 and September 15 of each year, beginning March 15, 2020. The net proceeds were used to fund a portion of the purchase of iPipeline Holdings, Inc.\nOn August 28, 2018, the Company completed a public offering of $700.0 aggregate principal amount of 3.65% senior unsecured notes due September 15, 2023 and $800.0 aggregate principal amount of 4.20% senior unsecured notes due September 15, 2028 (the “2018 Offering”). The notes bear interest at a fixed rate and are payable semi-annually in arrears on March 15 and September 15 of each year, beginning March 15, 2019.\nOn December 19, 2016, the Company completed a public offering of $500.0 aggregate principal amount of 2.80% senior unsecured notes due December 15, 2021 and $700.0 aggregate principal amount of 3.80% senior unsecured notes due December 15, 2026. The notes bear interest at a fixed rate and are payable semi-annually in arrears on June 15 and December 15 of each year, beginning June 15, 2017.\nOn December 7, 2015, the Company completed a public offering of $600.0 aggregate principal amount of 3.00% senior unsecured notes due December 15, 2020 and $300.0 aggregate principal amount of 3.85% senior unsecured notes due December 15, 2025. The notes bear interest at a fixed rate and are payable semi-annually in arrears on June 15 and December 15 of each year, beginning June 15, 2016.\nOn November 21, 2012, the Company completed a public offering of $500.0 aggregate principal amount of 3.125% senior unsecured notes due November 15, 2022. The notes bear interest at a fixed rate and are payable semi-annually in arrears on May 15 and November 15 of each year, beginning May 15, 2013.\nIn September 2009, the Company completed a public offering of $500.0 aggregate principal amount of 6.25% senior unsecured notes due September 1, 2019 (the “2019 Notes”). During 2018 a portion of the net proceeds of the 2018 Offering were used to redeem all of the $500.0 of outstanding 2019 Notes. The Company incurred a debt extinguishment charge in connection with the redemption of the 2019 Notes of $15.9, which represents the make-whole premium and unamortized deferred financing costs.\nRoper may redeem some or all of these notes at any time or from time to time, at 100% of their principal amount, plus a makewhole premium based on a spread to U.S. Treasury securities.\nThe Company’s senior notes are unsecured senior obligations of the Company and rank equally in right of payment with all of Roper’s existing and future unsecured and unsubordinated indebtedness. The notes are effectively subordinated to any of its existing and future secured indebtedness to the extent of the value of the collateral securing such indebtedness. The notes are not guaranteed by any of Roper’s subsidiaries and are effectively subordinated to all existing and future indebtedness and other liabilities of Roper’s subsidiaries.\nTotal debt at December 31 consisted of the following:", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 14, "source_local_id": "660c91e9fab1746851bf84a1ba856cd9", "source_table_id": "1291daaa275e574a7fd08db3068b388a"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:d34072f7f200a2d2a9a687ad5f413220", "source": "tatqa", "table_key": "68e636b30194f789af454426dda281a22f25ec84", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 4, "section_rows": [], "cells": [["", "Note", "2019", "(Restated)1 2018"], ["", "", "£m", "£m"], ["Revenue", "5", "355.1", "330.1"], ["Administrative expenses", "", "(112.3)", "(108.8)"], ["Share of profit from joint ventures", "16", "0.9", "–"], ["Operating profit", "6", "243.7", "221.3"], ["Finance costs", "9", "(10.2)", "(10.6)"], ["Profit on the sale of subsidiary", "10", "8.7", "–"], ["Profit before taxation", "", "242.2", "210.7"], ["Taxation", "11", "(44.5)", "(39.6)"], ["Profit for the year attributable to equity holders of the parent", "", "197.7", "171.1"], ["Basic earnings per share", "12", "", ""], ["From profit for the year (pence per share)", "", "21.00", "17.74"], ["Diluted earnings per share", "12", "", ""], ["From profit for the year (pence per share)", "", "20.94", "17.68"]]}, "native_split": "test", "question": "Which IFRS standard(s) did the Group adopt?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "IFRS 9 ‘Financial Instruments’, IFRS 15 ‘Revenue from Contracts with Customers’, and IFRS 16 ‘Leases’ from 1 April 2018", "answer_type": "span", "answer_from": "text", "paragraphs": "Consolidated income statement\nFor the year ended 31 March 2019\n1 The Group has adopted IFRS 9 ‘Financial Instruments’, IFRS 15 ‘Revenue from Contracts with Customers’, and IFRS 16 ‘Leases’ from 1 April 2018. The year ended 31 March 2018 has been restated for IFRS 16 which was implemented using the fully retrospective method. For further information on the impact of the change in accounting policies, see note 2 of these consolidated financial statements.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "d34072f7f200a2d2a9a687ad5f413220", "source_table_id": "3a79ab1e9645d84d9aa4255d692ef903"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:b7cb9deee91596540a0e61f6ef10b4d8", "source": "tatqa", "table_key": "f2f282e254aaea7042db5553ccb7ed5e73054689", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year Ended August 31,", ""], ["", "2019", "2018", "2017"], ["Net cash provided by (used in) operating activities", "$1,193,066", "$(1,105,448)", "$(1,464,085)"], ["Net cash (used in) provided by investing activities", "(872,454)", "1,240,914", "2,141,263"], ["Net cash used in financing activities", "(415,772)", "(47,044)", "(404,546)"], ["Effect of exchange rate changes on cash and cash equivalents", "554", "(20,392)", "5,228"], ["Net (decrease) increase in cash and cash equivalents", "$(94,606)", "$68,030", "$277,860"]]}, "native_split": "test", "question": "What was the Net cash used in financing activities in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "(415,772)", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Cash Flows\nThe following table sets forth selected consolidated cash flow information (in thousands):\nOperating Activities Net cash provided by operating activities during the fiscal year ended August 31, 2019 was primarily due to increased accounts payable, accrued expenses and other liabilities, decreased inventories and non-cash expenses, partially offset by increased contract assets and accounts receivable. The increase in accounts payable, accrued expenses and other liabilities is primarily due to the timing of collections on accounts receivable sold under the securitization programs and the timing of purchases and cash payments. The decrease in inventories is primarily due to the adoption of ASU 2014-09 and the reclassification to contract assets for revenue recognized for over time customers, partially offset by an increase in inventories to support expected sales levels in the first quarter of fiscal year 2020. The increase in contract assets is due to the adoption of ASU 2014-09 and the timing of revenue recognition for over time customers. The increase in accounts receivable is primarily driven by the amended and new securitization programs and higher sales and timing of collections.\nInvesting Activities Net cash used in investing activities during the fiscal year ended August 31, 2019 consisted primarily of capital expenditures principally to support ongoing business in the DMS and EMS segments and expenditures for assets acquired in connection with the initial and second closings of the acquisition of certain assets of JJMD, partially offset by proceeds and advances from the sale of property, plant and equipment and cash receipts on sold receivables under the asset-backed securitization programs.\nFinancing Activities Net cash used in financing activities during the fiscal year ended August 31, 2019 was primarily due to: (i) payments for debt agreements, (ii) the repurchase of our common stock, (iii) dividend payments and (iv) treasury stock minimum tax withholding related to vesting of restricted stock. Net cash used in financing activities was partially offset by: (i) borrowings under debt agreements and (ii) net proceeds from the exercise of stock options and issuance of common stock under the employee stock purchase plan.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "b7cb9deee91596540a0e61f6ef10b4d8", "source_table_id": "0b31ec7e0200c0354c067a2cf192f4af"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:70938e749d3e16304ae9e030eaabbc9b", "source": "tatqa", "table_key": "4402e2c2f4023360356fe24b23da5b90f2ebb60a", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Subscription revenue", "$542,968", "$473,052", "$396,764"], ["Percentage of subscription revenue to total revenue", "94.2%", "87.9%", "82.3%"], ["Professional services revenue", "$33,555", "$64,839", "$85,221"], ["Percentage of professional services to total revenue", "5.8%", "12.1%", "17.7%"], ["Total revenue", "$576,523", "537,891", "481,985"]]}, "native_split": "test", "question": "What was subscription revenue in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$542,968", "answer_type": "span", "answer_from": "table-text", "paragraphs": "The following table sets forth our sources of revenue for each of the periods indicated (in thousands, except for percentages):\nSubscription revenue increased by $69.9 million, or 15%, in 2019 when compared to 2018. Subscription revenue growth on a constant currency basis increased 16% in 2019 when compared to 2018. The increase was attributable to new business, which includes new customers, upsells, cross-sells, and renewals from existing customers.\nProfessional services revenue decreased by $31.3 million, or 48%, in 2019 when compared to 2018. The decrease of professional services revenue is attributable to the continued migration of implementation services to our global partners.\nSubscription revenue increased by $76.3 million, or 19%, in 2018 when compared to 2017. The increase was attributable to new business, which included new customers, upsells, and renewals from existing customers. Professional services revenue decreased by $20.4 million, or 24%, in 2018 when compared to 2017. The decrease of professional services revenue is attributable to the execution of our strategic initiative to migrate much of our implementation services to our global partners.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "70938e749d3e16304ae9e030eaabbc9b", "source_table_id": "596a9a79bc3d68a5fedca7987319a5f9"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:dfb9b6d511efb6add6566a70a60deddf", "source": "tatqa", "table_key": "86e7c15e982b784caa3c8800624dba28e8c06735", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["March 31,", "", ""], ["(in thousands)", "2019", "2018"], ["Raw materials", "$3,445", "$2,969"], ["Finished goods", "6,356", "6,253"], ["Total inventories", "$9,801", "$9,222"]]}, "native_split": "test", "question": "How is the market value calculated?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "based upon an estimated average selling price reduced by estimated costs of disposal", "answer_type": "span", "answer_from": "text", "paragraphs": "Inventories and Inventory Valuation\nInventories are stated at the lower of first-in, first-out (FIFO) cost or market value. Market value is based upon an estimated average selling price reduced by estimated costs of disposal. Should actual market conditions differ from the Company’s estimates, the Company’s future results of operations could be materially affected. Reductions in inventory valuation are included in Cost of revenue in the accompanying Consolidated Statements of Operations. The Company reviews inventory for excess quantities and obsolescence based on its best estimates of future demand, product lifecycle status and product development plans. The Company uses historical information along with these future estimates to reduce the inventory cost basis. Subsequent changes in facts and circumstances do not result in the restoration or increase in that newly established cost basis. Prices anticipated for future inventory demand are compared to current and committed inventory values.\nThe components of inventories are as follows:", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "dfb9b6d511efb6add6566a70a60deddf", "source_table_id": "6998ff3a821511af8a6c3d9b8c855502"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:6a37ffdabecf48f4fad76a3b07aa7981", "source": "tatqa", "table_key": "33c6019109c11ad881d8286878ec6eecc532c802", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 3, "section_rows": [], "cells": [["Name", "Age", "Position"], ["Avi Katz", "61", "President, General Counsel and Secretary since December 2012. Senior Vice President, General Counsel and Secretary from January 2008 to December 2012."], ["John Capogrossi", "66", "Vice President, Chief Financial Officer and Treasurer since January 2016. Vice President, Chief Financial Officer, Treasurer and Controller from March 2013 to January 2016. Vice President and Controller from January 2008 to March 2013."], ["Ravinder S. Girgla", "56", "Vice President and Controller since January 2016. Deputy Controller from February 2013 to January 2016. Assistant Controller from July 2008 to February 2013."]]}, "native_split": "test", "question": "Who is the oldest executive officer in the company?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "John Capogrossi", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Item 10. Directors, Executive Officers and Corporate Governance\nExecutive Officers of the Registrant\nThe following table sets forth information concerning the executive officers of Loral as of March 12, 2020.\nThe remaining information required under Item 10 will be presented in the Company’s 2020 definitive proxy statement which is incorporated herein by reference or by amendment to this Annual Report on Form 10‐K.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "6a37ffdabecf48f4fad76a3b07aa7981", "source_table_id": "616265ea38390d64cc296291b9de3fbc"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:4c9ee3a62029fd94fd8978737cb3276a", "source": "tatqa", "table_key": "ddcaf7cc3a0853d68920d1a63ec53d2e0b9fe6da", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Deferred income tax assets:", "", ""], ["Net operating loss carryforwards", "$23,030", "$25,745"], ["Tax credits", "52,902", "43,838"], ["Compensation", "18,791", "15,934"], ["Deferred revenue", "25,599", "27,587"], ["Research and development expense deferral", "—", "12,631"], ["Other", "4,065", "5,393"], ["Gross deferred income tax assets", "124,387", "131,128"], ["Less: valuation allowance", "(7,653)", "(20,415 )"], ["Net deferred income tax assets", "$ 116,734", "$ 110,713"], ["Deferred income tax liabilities:", "", ""], ["Depreciation and amortization", "$ (52,978)", "(60,872 )"], ["Deferred revenue", "(44,198)", "(54,508 )"], ["Total deferred income tax liabilities", "(97,176)", "(115,380 )"], ["Net deferred income taxes", "$ 19,558", "(4,667 )"], ["Deferred income taxes / liabilities included in the balance sheet are:", "", ""], ["Deferred income tax asset – noncurrent", "$ 51,611", "$ 27,048"], ["Deferred income tax liability – noncurrent", "(32,053)", "(31,715 )"], ["Net deferred income taxes", "$ 19,558", "(4,667 )"]]}, "native_split": "test", "question": "How much was Net operating loss carryforwards in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$23,030", "answer_type": "span", "answer_from": "table-text", "paragraphs": "The deferred tax assets and liabilities result from differences in the timing of the recognition of certain income and expense items for tax and financial accounting purposes. The sources of these differences at each balance sheet date are as follows (in thousands):\nIn assessing the realizability of deferred tax assets, the Company considers whether it is more likely than not that some portion or all of the deferred tax assets will not be realized. The ultimate realization of deferred tax assets is dependent upon the generation of future taxable income during the periods in which those temporary differences become deductible. The Company considers projected future taxable income, carryback opportunities, and tax planning strategies in making this assessment. Based upon the level of historical taxable income and projections for future taxable income over the periods which the deferred tax assets are deductible, the Company believes it is more likely than not that it will realize the benefits of these deductible differences, net of the valuation allowances recorded. During the year ended December 31, 2019, the Company decreased its valuation allowance by $12.8 million which relates to a reduction in the valuation allowance on U.S. foreign tax credits offset by an increase in valuation allowance on foreign net operating losses.\nAt December 31, 2019, the Company had domestic federal tax net operating losses (“NOLs”) of $65.9 million, which will begin to expire in 2020. The Company had deferred tax assets equal to $1.4 million related to domestic state tax NOLs which will begin to expire in 2020. The Company does not have any valuation allowance against the federal tax NOLs but has provided a $1.2 million valuation allowance against the deferred tax asset associated with the state NOLs. The Company had foreign tax NOLs of $30.4 million, of which $28.1 million may be utilized over an indefinite life, with the remainder expiring over the next 17 years. The Company has provided a $0.7 million valuation allowance against the deferred tax asset associated with the foreign NOLs.\nThe Company had U.S. foreign tax credit carryforwards at December 31, 2019, of $40.7 million, for which an $1.2 million valuation allowance has been provided. The U.S. foreign tax credits will begin to expire in 2022. The Company had foreign tax credit carryforwards in other foreign jurisdictions at December 31, 2019, of $1.9 million, of which $1.3 million may be utilized over an indefinite life, with the remainder expiring over the next seven years. The Company has provided a $1.2 million valuation allowance against the tax benefit associated with these foreign credits. The Company also has domestic federal and state general business tax credit carryforwards at December 31, 2019, of $15.7 million and $0.8 million, respectively, which will begin to expire in 2020 and 2022, respectively.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "4c9ee3a62029fd94fd8978737cb3276a", "source_table_id": "0551beccb943904e051f6f2a1519bd3a"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:b5e321208e194fa725232420975d1612", "source": "tatqa", "table_key": "306acb8da2986075e4460e30433ab50d2abeff43", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 6, "section_rows": [], "cells": [["", "Years ended December 31,", "", "", "2019 vs 2018", "2018 vs 2017"], ["", "2019", "2018", "2017", "% Change", "% Change"], ["", "(in thousands, except percentages)", "", "", "", ""], ["Sales and marketing", "$ 472,841", "$ 350,069", "$ 225,694", "35.1 %", "55.1 %"], ["Percentage of total revenues", "30.0 %", "32.6 %", "33.5 %", "", ""]]}, "native_split": "test", "question": "Between year ended 2018 and 2019, which year had a higher sales and marketing expense?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2019", "answer_type": "span", "answer_from": "table", "paragraphs": "Operating Expense\nSales and Marketing\nSales and marketing expenses increased $122.8 million, or 35.1%, for the year ended December 31, 2019 compared to the same period in 2018, due to an increase of $70.4 million in expenditures on marketing programs to support the growth of our business, such as advertisements on search engines and social media, brand campaigns, event sponsorships and payments to partners. Employee-related costs increased $48.7 million ($14.1 million of which related to stock-based compensation and related payroll taxes) to support the growth of the business including in Shopify Plus and International operations. Computer hardware and software costs increased by $3.7 million, largely due to the growth in sales and marketing headcount\nSales and marketing expenses increased $124.4 million, or 55.1%, for the year ended December 31, 2018 compared to the same period in 2017, primarily due to an increase of $80.7 million in employee-related costs. In addition to employee-related costs, marketing costs increased by $39.7 million and computer hardware and software costs increased by $4.0 million.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "b5e321208e194fa725232420975d1612", "source_table_id": "6ad2ec5c0b711b3fb827cb77b1ebecbe"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:ffb566812f8006fa65d3734385f680bd", "source": "tatqa", "table_key": "7a878c4eb3435f958999b08c4b4decb993c018d2", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Depreciation expense", "$2,540", "$2,320", "$2,149"], ["Amortization expense", "4,764", "2,158", "1,834"], ["Impairment losses", "—", "19", "78"]]}, "native_split": "test", "question": "What was the depreciation expense in 2017?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2,149", "answer_type": "span", "answer_from": "table-text", "paragraphs": "GreenSky, Inc. NOTES TO CONSOLIDATED FINANCIAL STATEMENTS — (Continued) (United States Dollars in thousands, except per share data, unless otherwise stated)\nNote 6. Property, Equipment and Software\nThe following table shows depreciation and amortization expense, as well as recorded impairment losses related to abandoned capitalized software projects that are recorded within general and administrative expense in the Consolidated Statements of Operations. We determined that these software projects would not generate future cash flows through use or disposal to a third party and, as such, the fair value as of the respective reporting dates was $0.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "ffb566812f8006fa65d3734385f680bd", "source_table_id": "414d3b3dabd0898d3de089e2cc2d6bf5"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:67ce9efa3d59d51dc6f2c7a697bcbbe8", "source": "tatqa", "table_key": "dbf9ee42df2c83fa04bb8d23c599cd87baddc467", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "Fiscal Year", "", "Variance in", ""], ["(In millions, except for percentages)", "2018", "2017", "Dollars", "Percent"], ["Net revenues", "$2,554", "$2,355", "$199", "8%"], ["Percentage of total net revenues", "53%", "59%", "", ""], ["Operating income", "$473", "$187", "$286", "153%"], ["Operating margin", "19%", "8%", "", ""]]}, "native_split": "test", "question": "What was the variance in percent for Net revenues from fiscal 2017 to fiscal 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "8%", "answer_type": "span", "answer_from": "table", "paragraphs": "Segment operating results\nEnterprise Security segment\nRevenue increased $199 million primarily due to increases of $331 million in revenue from sales of our network and web security solutions and $36 million from sales of endpoint and information protection solutions, partially offset by a $184 million decrease in revenue as a result of the divestiture of our WSS and PKI solutions. Revenue during fiscal 2018 was also unfavorably affected by a shift in the mix of sales towards subscription and cloud-delivered solutions subject to ratable revenue recognition, which resulted in less in-period recognized revenue and more revenue deferred to the balance sheet as compared to fiscal 2017. Operating income increased $286 million primarily due to higher revenue discussed above, a $51 million decrease in sales and marketing expenses and a $38 million decrease in cost of revenues.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "67ce9efa3d59d51dc6f2c7a697bcbbe8", "source_table_id": "a916ea80f6a9a839b0e52ac9f5efe96f"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:c7be66d87ca32b1632bd7e527ef03d9c", "source": "tatqa", "table_key": "cc846c0b0bc1389ee456313832fd2341af8caf1c", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2018", "2019"], ["Loans due from subsidiaries - non-current portion", "46,698", "45,377"], ["Loans due from subsidiaries - current portion", "2,064", "2,123"], ["Total", "48,762", "47,500"]]}, "native_split": "test", "question": "What is the schedule of repayment of the loan?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "24 annual installments of US$2 million, starting December 31, 2018, followed by a final installment of US$5.3 million on December 31, 2043", "answer_type": "span", "answer_from": "text", "paragraphs": "NOTE 5. INVESTMENTS AND LOANS TO SUBSIDIARIES\nInterest relates mainly to a subsidiary and is based on the Bank of America’s prime rate with a rise of two percent points. The repayment schedule of the loan is as follows: 24 annual installments of US$2 million, starting December 31, 2018, followed by a final installment of US$5.3 million on December 31, 2043.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 2, "source_local_id": "c7be66d87ca32b1632bd7e527ef03d9c", "source_table_id": "48adf518eb23b8900b86a6c63066d799"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:78bb28f7796b254c71295e12c879b87b", "source": "tatqa", "table_key": "517cadd3849643cb776b43b93bda2c94cd0d5384", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["(In thousands, except for per share amounts)", "2019", "2018", "2017"], ["Numerator", "", "", ""], ["Net Income (Loss)", "$(52,982)", "$(19,342)", "$23,840"], ["Denominator", "", "", ""], ["Weighted average number of shares—basic", "47,836", "47,880", "48,153"], ["Effect of dilutive securities:", "", "", ""], ["Stock options", "—", "—", "406"], ["Restricted stock and restricted stock units", "—", "—", "140"], ["Weighted average number of shares—diluted", "47,836", "47,880", "48,699"], ["Earnings (loss) per share—basic", "$(1.11)", "$(0.40)", "$0.50"], ["Earnings (loss) per share—diluted", "$(1.11)", "$(0.40)", "$0.49"]]}, "native_split": "test", "question": "What was the Weighted average number of shares—basic in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "47,880", "answer_type": "span", "answer_from": "table", "paragraphs": "Note 17 – Earnings (Loss) per Share\nA summary of the calculation of basic and diluted earnings (loss) per share for the years ended December 31, 2019, 2018 and 2017 is as follows:\nFor each of the years ended December 31, 2019 and 2018, 5.7 million and 2.5 million, respectively, shares of unvested stock options, PSUs, RSUs and restricted stock were excluded from the calculation of diluted EPS due to their anti-dilutive effect.\nFor the year ended December 31, 2017, 3.2 million stock options were outstanding but were not included in the computation of diluted earnings (loss) per share because the options’ exercise prices were greater than the average market price of the common shares, therefore making them anti-dilutive under the treasury stock method.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "78bb28f7796b254c71295e12c879b87b", "source_table_id": "5eddb047f49ae0865aebe818c1837158"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:3c5e5c5f876eb8b99638dc89b36e0092", "source": "tatqa", "table_key": "fba8d12228d7f1a23b5330de55eb65f80a62a8d9", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["", "High", "Low", "Dividends\nDeclared Per\nShare"], ["Fiscal Year Ended April 27, 2019", "", "", ""], ["First Quarter", "$45.45", "$37.70", "$0.11"], ["Second Quarter", "41.30", "27.65", "0.11"], ["Third Quarter", "33.98", "20.99", "0.11"], ["Fourth Quarter", "32.22", "25.11", "0.11"], ["Fiscal Year Ended April 28, 2018", "", "", ""], ["First Quarter", "$44.95", "$36.05", "$0.09"], ["Second Quarter", "46.75", "36.75", "0.09"], ["Third Quarter", "48.44", "39.00", "0.11"], ["Fourth Quarter", "42.10", "36.95", "0.11"]]}, "native_split": "test", "question": "When was the dividend declared in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "June 13, 2019", "answer_type": "span", "answer_from": "text", "paragraphs": "Item 5. Market for Registrant’s Common Equity, Related Stockholder Matters and Issuer Purchases of Equity Securities\nOur common stock is traded on the New York Stock Exchange under the symbol \"MEI.\" The following is a tabulation of high and low sales prices for the periods presented and cash dividends declared per share.\nOn June 13, 2019, the Board of Directors declared a dividend of $0.11 per share of common stock, payable on July 26, 2019, to holders of record on July 12, 2019. As of June 18, 2019, the number of record holders of our common stock was 397.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "3c5e5c5f876eb8b99638dc89b36e0092", "source_table_id": "ef37215f6a63e5f06c6b32f7c3db7aad"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:6586914ac61d7cace8775b43ab595394", "source": "tatqa", "table_key": "2d6a9683ae7120e98b1fc721cf92f808973835d4", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "2019", "2018", "$ CHANGE", "% CHANGE"], ["External service revenues", "6,427", "6,269", "158", "2.5%"], ["Inter-segment service revenues", "49", "48", "1", "2.1%"], ["Total operating service revenues", "6,476", "6,317", "159", "2.5%"], ["External product revenues", "2,660", "2,497", "163", "6.5%"], ["Inter-segment product revenues", "6", "4", "2", "50.0%"], ["Total operating product revenues", "2,666", "2,501", "165", "6.6%"], ["Total Bell Wireless revenues", "9,142", "8,818", "324", "3.7%"]]}, "native_split": "test", "question": "What drove the change in Bell Wireless operating revenues in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "greater postpaid and prepaid service revenues and higher product revenues", "answer_type": "span", "answer_from": "text", "paragraphs": "BELL WIRELESS RESULTS\nREVENUES\nBell Wireless operating revenues increased by 3.7% in 2019, compared to 2018, driven by greater postpaid and prepaid service revenues and higher product revenues.\nService revenues increased by 2.5% in 2019, compared to last year, driven by: • Continued growth in our postpaid and prepaid subscriber base coupled with rate increases • A greater mix of customers subscribing to higher-value monthly plans including unlimited data plans launched in June 2019 • The favourable year-over-year impact from the 2018 CRTC retroactive decision on wireless domestic wholesale roaming rates\nThese factors were partly offset by: • Greater sales of premium handsets and more customers subscribing to higher-value monthly plans • Lower data and voice overages driven by increased customer adoption of monthly plans with higher data allotments and richer voice plans\nProduct revenues increased by 6.6% in 2019, compared to last year, driven by greater sales of premium handsets and the impact of higher-value rate plans in our sales mix.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 6, "source_local_id": "6586914ac61d7cace8775b43ab595394", "source_table_id": "becd30d40294d901789bd1e35c057cc7"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:0ea094653e8438a4788d9638d359a491", "source": "tatqa", "table_key": "55a3ab34eeb0eea49d87996ed4f3ab1b284929f1", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Trade accounts receivable, net, current (1)", "$375,136", "$335,377"], ["Trade accounts receivable, net, noncurrent (2)", "26,496", "15,948"], ["", "$401,632", "$351,325"]]}, "native_split": "test", "question": "Where is Trade accounts receivable, net, noncurrent included in?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "in “Deferred charges and other assets” in the accompanying Consolidated Balance Sheets.", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Trade Accounts Receivable\nThe Company’s trade accounts receivable, net, consisted of the following (in thousands):\n(1) Included in “Receivables, net” in the accompanying Consolidated Balance Sheets.\n(2) Included in “Deferred charges and other assets” in the accompanying Consolidated Balance Sheets.\nThe Company’s noncurrent trade accounts receivable result from (1) contracts with customers that include renewal provisions, and (2) contracts with customers under multi-year arrangements. For contracts that include renewal provisions, revenue is recognized up-front upon satisfaction of the associated performance obligations, but payments are received upon renewal. Renewals occur in bi-annual and annual increments over the associated expected contract term, the majority of which range from two to five years. The Company’s contracts with customers under multi-year arrangements generally have three-year terms and are invoiced annually at the beginning of each annual coverage period. The Company records a receivable related to revenue recognized under multi-year arrangements as the Company has an unconditional right to invoice and receive payment in the future related to these arrangements.\nWhere the timing of revenue recognition differs from the timing of invoicing and payment, the Company has determined that its contracts do not include a significant financing component. A substantial amount of the consideration promised by the customer under the contracts that include renewal provisions is variable, and the amount and timing of that consideration varies based on the occurrence or nonoccurrence of future events that are not substantially within the Company’s control. With respect to multi-year year arrangements, there is minimal difference between the consideration received and the cash selling price, any offered discounts are driven by volume, and the contracts are of short duration resulting in insignificant interest. Thus, the primary purpose of the invoicing terms on the multi-year arrangements is to provide the customer with a simplified and predictable way of purchasing certain products, not to provide financing or to receive financing from the Company’s customer.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 6, "source_local_id": "0ea094653e8438a4788d9638d359a491", "source_table_id": "aad7041cfc521e55841bb2b62200bad1"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:56ff97230978b4ccbb32b8df028391c3", "source": "tatqa", "table_key": "66d323fe37bfb741a0746dd5b88c3645fdc33208", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "", "30 June 2019", "30 June 2018"], ["", "Note", "$'000", "$'000"], ["EXPENSE", "", "", ""], ["Finance costs", "4(a)", "(54,897)", "(25,803)"], ["Data centre rent paid to APDC (included in Data centre facility costs)", "4(b)", "-", "(13,785)"], ["APDC transaction costs (included in Professional fees)", "4(c)", "(5,459)", "-"], ["Landholder duty on acquisition of APDC properties (included in Office and administrative expenses)", "4(c)", "(3,498)", "-"]]}, "native_split": "test", "question": "How much was the landholder duty on acquisition of APDC properties in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3,498", "answer_type": "span", "answer_from": "table", "paragraphs": "4 Expenses\nThe Group has identified a number of significant expense items below that impacted financial performance for the year:\n(a) Finance costs\nIncluded in finance costs are costs related to unsecured notes on issue and interest expense on lease liabilities.\nRefer to note 16 for details on unsecured notes on issue and note 12 for details on interest expense on lease liabilities for the year.\n(b) Data centre rent paid to APDC\nNEXTDC Limited acquired Asia Pacific Data Centre (\"APDC\") on 18 October 2018 (refer to note 26). Prior to acquisition, APDC was the landlord of three of NEXTDC’s data centre facilities: M1 Melbourne, S1 Sydney and P1 Perth. For the year ended 30 June 2018, NEXTDC paid rent and ancillary amounts to APDC totalling $13.8 million that was included in the Consolidated Statement of Comprehensive Income in Data centre facility costs. On early adoption of AASB 16 from 1 July 2018, this rent ceased to be a Data centre facility cost, and became a depreciation expense and finance cost, until 18 October 2018, when the three leases were derecognised on acquisition.\n(c) APDC acquisition costs\nA number of acquisition related costs were incurred as a result of the acquisition of APDC. Refer note 26 for further details.", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 9, "source_local_id": "56ff97230978b4ccbb32b8df028391c3", "source_table_id": "73842f616f18b30fe624ac4a4281a91d"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:363e5b4e5f1d5e1fd5819d862a4340cf", "source": "tatqa", "table_key": "f991a5bf67940fe66d1306ec5562e59061e96c3e", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["Name", "Fees Earned or Paid in Cash ($)", "Stock Awards ($)", "Option Awards ($)", "Total($)"], ["Richard S. Hill", "$106,000", "$149,987", "—", "$255,987"], ["Darcy Antonellis", "$55,333", "$149,987", "—", "$205,320"], ["David C. Habiger", "$70,000", "$149,987", "—", "$219,987"], ["V. Sue Molina", "$81,000", "$149,987", "—", "$230,987"], ["George A. Riedel", "$68,333", "$149,987", "—", "$218,320"], ["Christopher A. Seams", "$82,000", "$149,987", "—", "$231,987"]]}, "native_split": "test", "question": "How does a non-employee director receive his/her restricted stock unit award or option grant?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "on the date of his or her initial appointment to the Board of Directors equal to the pro-rated amount of the annual grant", "answer_type": "span", "answer_from": "text", "paragraphs": "A non-employee director who is initially appointed after any annual meeting of stockholders will receive a restricted stock unit award or option grant on the date of his or her initial appointment to the Board of Directors equal to the pro-rated amount of the annual grant\nAnnual option grants and restricted stock unit awards (or any pro-rated grants for directors initially appointed between annual meetings) vest on the earlier to occur of the first anniversary of the date of grant or the next annual meeting of stockholders. No portion of an option automatically granted to a director is exercisable after the tenth anniversary after the date of option grant. Additionally, an option automatically granted to a director may be exercisable after the termination of the director’s services as described in the option agreement, generally ending three months after such termination.\nThe following table shows compensation information for our non-employee directors for fiscal year 2019.\n2019 Director Compensation Table\n(1) The amounts reflected in this column represent the aggregate grant date fair value for stock awards granted to our non-employee directors in 2019, measured in accordance with ASC 718, excluding the effect of estimated forfeitures, and do not reflect whether the recipient has actually realized a financial benefit from these awards. For the methodology of how the aggregate grant date fair value amount is calculated, please see Note 13 of the Notes to Consolidated Financial Statements included in this Form 10-K. The aggregate number of shares subject to unvested restricted stock unit awards outstanding for each non-employee director at December 31, 2019 was: Mr. Hill: 5,889; Ms. Antonellis: 5,889; Mr. Habiger: 5,889; Ms. Molina: 5,889; Mr. Riedel: 5,889 and Mr. Seams: 5,889. None of the non-employee directors held any stock options as of December 31, 2019.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "363e5b4e5f1d5e1fd5819d862a4340cf", "source_table_id": "9246b58ea8656eecf7739094d072f0b8"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:d133de7f51dfb786b61d27cc822f35e3", "source": "tatqa", "table_key": "7d721ea55efe74a9eeb4e367daf8ede74f0f9d24", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["Application", "2017", "2018", "2019"], ["", "%", "%", "%"], ["Communication", "48.6", "45.2", "52.2"], ["Consumer", "29.4", "28.6", "26.4"], ["Computer", "13.1", "16.3", "13.6"], ["Others", "8.9", "9.9", "7.8"], ["Total", "100.0", "100.0", "100.0"]]}, "native_split": "test", "question": "What does the primary business of the company include?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "manufacture, or “fabrication”, of semiconductors, sometimes called “chips” or “integrated circuits”, for others.", "answer_type": "span", "answer_from": "text", "paragraphs": "History and Development of the Company\nOur legal and commercial name is United Microelectronics Corporation, commonly known as “UMC”. We were incorporated under the R.O.C. Company Law as a company limited by shares in May 1980 and our common shares were listed on the Taiwan Stock Exchange in 1985. Our principal executive office is located at No. 3 Li-Hsin Road II, Hsinchu Science Park, Hsinchu, Taiwan, Republic of China, and our telephone number is 886-3-578-2258.\nOur Internet website address is www.umc.com. The information on our website does not form part of this annual report. Our ADSs have been listed on the NYSE under the symbol “UMC” since September 19, 2000. In 2019, we were ranked among the top 5% of companies for a fifth consecutive year in the Corporate Governance Evaluation conducted by the Taiwan Stock Exchange and Taipei Exchange. The assessment was conducted across over 1,400 public companies in Taiwan.\nWe are one of the world’s largest independent semiconductor foundries and a leader in semiconductor manufacturing process technologies. Our primary business is the manufacture, or “fabrication”, of semiconductors, sometimes called “chips” or “integrated circuits”, for others. Using our own proprietary processes and techniques, we make chips to the design specifications of our many customers. Our company maintains a diversified customer base across industries, including communication devices, consumer electronics, computer, and others, while continuing to focus on manufacturing for high growth, large volume applications, including networking, telecommunications, internet, multimedia, PCs and graphics.\nWe sell and market mainly wafers which in turn are used in a number of different applications by our customers. The following table presented the percentages of our wafer sales by application for the years ended December 31, 2017, 2018 and 2019.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "d133de7f51dfb786b61d27cc822f35e3", "source_table_id": "03a98443a7286862ffe1b6bcc2193e54"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:3a54775eea90e493ada7cc37b99b5062", "source": "tatqa", "table_key": "5dbd6eef3ba8ed70416ba12a295f0bd45a83a4f5", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 3, "section_rows": [], "cells": [["", "December 31, 2019", "December 31,2018"], ["Accounts receivable", "$68,309", "$76,376"], ["Less: provision for doubtful receivables", "(16,377)", "(16,086)"], ["Accounts receivable, net", "$51,932", "$60,290"]]}, "native_split": "test", "question": "What was the provision for doubtful receivables in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "(16,086)", "answer_type": "span", "answer_from": "table-text", "paragraphs": "NAVIOS MARITIME HOLDINGS INC. NOTES TO THE CONSOLIDATED FINANCIAL STATEMENTS (Expressed in thousands of U.S. dollars — except share data)\nNOTE 5: ACCOUNTS RECEIVABLE, NET\nAccounts receivable consisted of the following:", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "3a54775eea90e493ada7cc37b99b5062", "source_table_id": "5208576a85192612b24f629769981aa8"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:6cdac845a447b3460bb9697592ca5c7e", "source": "tatqa", "table_key": "0c97825f0e83be1f4b0512a69ef96c5bce9ab5fa", "grid": {"TD": 1, "LD": 0, "nrows": 4, "ncols": 4, "section_rows": [], "cells": [["(IN MILLIONS)", "2019", "2018", "2017"], ["Net cash from operating activities", "$1,066", "$1,058", "$1,310"], ["Cash and short-term marketable securities", "$454", "$524", "$656"], ["Revolving credit facility", "$850", "$850", "$575"]]}, "native_split": "test", "question": "What caused the increase in cash flows during the year ended December 31, 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "lower employee annual incentive payments, lower retailer investments and lower restructuring payments, partially offset by working capital timing and higher interest and tax payments", "answer_type": "span", "answer_from": "text", "paragraphs": "Liquidity and Capital Resources\nCash flows from operations provided a source of funds of $1,066 million, $1,058 million and $1,310 million during the years ended December 31, 2019, 2018 and 2017, respectively. This increase was driven primarily by lower employee annual incentive payments, lower retailer investments and lower restructuring payments, partially offset by working capital timing and higher interest and tax payments during the year ended December 31, 2019.\nWe provide for additional liquidity through several sources, including maintaining an adequate cash balance, access to global funding sources and a committed revolving credit facility. The following table provides a summary of the major sources of liquidity for the years ended December 31, 2019, 2018 and 2017:\nOf the $454 million in cash and cash equivalents at December 31, 2019, approximately $383 million was held in jurisdictions outside the U.S. We regularly review the amount of cash and cash equivalents held outside of the U.S. to determine the amounts necessary to fund the current operations of our foreign operations and their growth initiatives and amounts needed to service our U.S. indebtedness and related obligations.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "6cdac845a447b3460bb9697592ca5c7e", "source_table_id": "d69f61835b35ec05879669dc41ff57aa"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:d4d53494d28340bec9b7ccd2e1df4b1c", "source": "tatqa", "table_key": "f56be4ff1befb872d73c560f70e2aa63b7a041a7", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "2019", "2018"], ["Revenue", "£m", "£m"], ["UK", "349.9", "324.9"], ["Ireland", "5.2", "5.2"], ["Total revenue", "355.1", "330.1"]]}, "native_split": "test", "question": "In which year was revenue in the UK larger?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2019", "answer_type": "span", "answer_from": "table", "paragraphs": "Geographic information\nThe Group is domiciled in the UK and the following tables detail external revenue by location of customers, trade receivables and non-current assets (excluding deferred tax) by geographic area:\nDue to the large number of customers the Group serves, there are no individual customers whose revenue is greater than 10% of the Group’s total revenue in all periods presented in these financial statements.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "d4d53494d28340bec9b7ccd2e1df4b1c", "source_table_id": "9fe2506a4389f5a61511c915c3323f10"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:b4470f5ef49cd05739b7bdf49a8ad6d4", "source": "tatqa", "table_key": "19290c915c755610fe5f095b567eb650097aa06c", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 5, "section_rows": [], "cells": [["", "2019", "", "2018", ""], ["", "United States", "Foreign", "United States", "Foreign"], ["", "", "(in millions)", "", ""], ["Projected benefit obligation", "$55.3", "$44.0", "$50.8", "$39.1"], ["Accumulated benefit obligation", "53.2", "39.9", "48.6", "35.6"], ["Fair value of plan assets", "—", "1.6", "—", "1.4"]]}, "native_split": "test", "question": "What was the  accumulated benefit obligation for the United States defined benefit pension plans in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$198.2 million", "answer_type": "span", "answer_from": "text", "paragraphs": "The accumulated benefit obligation for the United States defined benefit pension plans was $198.2 million and $172.8 million at December 31, 2019 and 2018, respectively. The accumulated benefit obligation for foreign defined benefit pension plans was $39.9 million and $35.6 million at December 31, 2019 and 2018, respectively.\nInformation for pension plans with an accumulated benefit obligation in excess of plan assets as of December 31:", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 2, "source_local_id": "b4470f5ef49cd05739b7bdf49a8ad6d4", "source_table_id": "adf4737e7f582acd0e93b35406b0322f"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:f57289cb36aad8d9f704b97368a28bcb", "source": "tatqa", "table_key": "498ff9cb6c92ee50f5da35a6f3660e4ee7e829e5", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 4, "section_rows": [], "cells": [["", "Fiscal 2019", "Fiscal 2018", "% Change"], ["", "", "(in millions)", ""], ["Sales", "$  849.8", "$  670.7", "27 %"], ["Operating income", "77.2", "60.4", "28"], ["Adjusted EBITDA", "110.5", "73.3", "51"]]}, "native_split": "test", "question": "What resulted in the increase in the amortization of purchased intangibles included in the CTS operating results?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "amortization of purchased intangibles for companies acquired by CTS in fiscal year 2019.", "answer_type": "span", "answer_from": "text", "paragraphs": "SEGMENT RESULTS\nCubic Transportation Systems\nSales: CTS sales increased 27% to $849.8 million in 2019 compared to $670.7 million in 2018, including the impact of the adoption of ASC 606. The increase in sales was primarily driven by growth in both organic and inorganic business in North America. Sales in 2019 were higher in the U.S. primarily due to system development on contracts in New York, Boston, and the San Francisco Bay Area. Businesses acquired by CTS during fiscal year 2019, whose operations are all located in the U.S., had sales of $74.4 million in fiscal year 2019. Sales increased slightly in Australia between fiscal years 2018 and 2019 as increased system development work on a contract in Brisbane was partially offset by the negative impact of foreign currency exchange rates as well as a decrease in service sales. Sales were lower in the UK primarily due to a decrease in system development work in London and the negative impact of currency exchange rates. The average exchange rates between the prevailing currencies in our foreign operations and the U.S. dollar resulted in a decrease in CTS sales of $22.2 million for 2019 compared to 2018, primarily due to the strengthening of the U.S. dollar against the British pound and Australian dollar.\nAmortization of Purchased Intangibles: Amortization of purchased intangibles included in the CTS operating results totaled $22.0 million in 2019 and $5.2 million in 2018. The increase is due to the amortization of purchased intangibles for companies acquired by CTS in fiscal year 2019.\nOperating Income: CTS operating income increased 28% in 2019 to $77.2 million compared to $60.4 million in 2018. The increase in operating income was primarily caused by higher margins on increased work on development projects in New York, Boston, the San Francisco Bay Area and Brisbane, as well as the impact of the adoption of ASC 606. These increases in operating income were partially offset by operating losses incurred by businesses acquired by CTS in fiscal 2019 as well as the negative impact of changes in foreign currency exchange rates. Businesses acquired by CTS in fiscal years 2019 incurred operating losses of $10.1 million in fiscal 2019, which included acquisition transaction costs of $8.1 million and amortization of intangible assets totaling $19.3 million. The average exchange rates between the prevailing currency in our foreign operations and the U.S. dollar resulted in a decrease in CTS operating income of $3.6 million for 2019 compared to 2018.\nAdjusted EBITDA: CTS Adjusted EBITDA increased 51% to $110.5 million in 2019 compared to $73.3 million in 2018. The increase in Adjusted EBITDA was primarily driven by the same factors that drove the increase in operating income described above excluding the increases in amortization of purchased intangibles and acquisition transaction costs which are excluded from Adjusted EBITDA. Adjusted EBITDA for CTS increased by $2.3 million in 2019 as a result of the adoption of the new revenue recognition standard.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 6, "source_local_id": "f57289cb36aad8d9f704b97368a28bcb", "source_table_id": "0fa66bdde5dce465760cf558173d40c6"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:c0d1c8f4c3ffd0cce87dc513fc69aca1", "source": "tatqa", "table_key": "50b403a90bd54dbaf455558773dc58283884420a", "grid": {"TD": 1, "LD": 0, "nrows": 6, "ncols": 4, "section_rows": [], "cells": [["", "", "Year Ended December 31,", ""], ["", "2019", "2018", "2017"], ["Warranty reserve, beginning of period", "$8,220", "$8,306", "$2,158"], ["Provisions made to warranty reserve during the period", "13,708", "11,775", "16,597"], ["Charges against warranty reserve during the period", "(11,574)", "(11,861)", "(10,449)"], ["Warranty reserve end of period", "$10,354", "8,220", "8,306"]]}, "native_split": "test", "question": "What are 3 factors that can affect warranty obligation?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "Factors that affect the warranty obligation include product failure rates, material usage and service delivery costs incurred in correcting product failures.", "answer_type": "span", "answer_from": "text", "paragraphs": "COMMITMENTS AND CONTINGENCIES\nWarranties\nThe Company’s standard warranty obligation to its customers provides for repair or replacement of a defective product at the Company’s discretion for a period of time following purchase, generally between 12 and 24 months. Factors that affect the warranty obligation include product failure rates, material usage and service delivery costs incurred in correcting product failures. In addition, from time to time, specific warranty accruals may be made if unforeseen technical problems arise. The estimated cost associated with fulfilling the Company’s warranty obligation to customers is recorded in cost of revenue.\nChanges in the Company’s warranty liability, which is included as a component of accrued liabilities on the consolidated balance sheets, are set forth in the table below (in thousands):", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "c0d1c8f4c3ffd0cce87dc513fc69aca1", "source_table_id": "5e5bec4bc6101631dc4209ce562b9418"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:d3d6b5d96102cccaeeb186b1e3a16b4c", "source": "tatqa", "table_key": "e9f083dc345b4eb44921bbe4baf94753410e8c18", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 4, "section_rows": [], "cells": [["", "", "Years Ended December 31,", ""], ["Process Technologies", "2017", "2018", "2019"], ["", "%", "%", "%"], ["14 nanometers and under", "0.9", "2.6", "0.0"], ["28 nanometers", "16.2", "12.6", "11.3"], ["40 nanometers", "28.4", "25.3", "23.1"], ["65 nanometers", "12.3", "12.5", "14.7"], ["90 nanometers", "4.9", "8.3", "13.6"], ["0.11/0.13 micron", "11.5", "11.6", "12.6"], ["0.15/0.18 micron", "12.4", "13.7", "13.1"], ["0.25/0.35 micron", "10.0", "10.1", "8.7"], ["0.50 micron or higher", "3.4", "3.3", "2.9"], ["Total", "100.0", "100.0", "100.0"]]}, "native_split": "test", "question": "What was the percentage of 14 nanometers and under in 2017?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "0.9", "answer_type": "span", "answer_from": "table", "paragraphs": "Change in Product Mix and Technology Migration\nBecause the price of wafers processed with different technologies varies significantly, the mix of wafers that we produce is among the primary factors that affect our revenues and profitability. The value of a wafer is determined principally by the complexity and performance of the processing technology used to produce the wafer, as well as by the yield and defect density. Production of devices with higher levels of functionality and performance, with better yields and lower defect density as well as with greater system-level integration requires better manufacturing expertise and generally commands higher wafer prices. The increase in price generally has more than offset associated increases in production cost once an appropriate economy of scale is reached.\nPrices for wafers of a given level of technology generally decline over the processing technology life cycle. As a result, we have continuously been migrating to increasingly sophisticated technologies to maintain the same level of profitability. We began our volume production with 65-nanometer and 40-nanometer technologies in 2006 and 2009, respectively. We introduced our 28-nanometer technology to customers in 2011 and started large-scale commercial production in 2014. Our 28nm and below technologies contributed approximately 17.1%, 15.2% and 11.3% of our foundry revenue in 2017, 2018 and 2019 respectively.", "scale": "percent", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "d3d6b5d96102cccaeeb186b1e3a16b4c", "source_table_id": "0b96522d90d672fb9a9575684feab0a6"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:7030e0a5e64490b1f47f9c695f68edde", "source": "tatqa", "table_key": "ce742ff9e26c9dd6594ce06d5064db522f88836c", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 4, "section_rows": [], "cells": [["", "", "Fiscal Year", ""], ["(In thousands, except percentages)", "2019", "2018", "2017"], ["Interest income", "$2,702", "$3,057", "$2,100"], ["Interest expense", "(53,353)", "(108,011)", "(90,288)"], ["Other Income (expense):", "", "", ""], ["Other, net", "174,734", "55,314", "(87,645)"], ["Other income (expense), net", "$124,083", "$(49,640)", "$(175,833)"], ["As a percentage of revenue", "7%", "(3)%", "(10)%"]]}, "native_split": "test", "question": "What was the reason for the change in other income from 2018 to 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "Due to a $158.3 million gain on an equity investment with readily determinable fair value in fiscal 2019", "answer_type": "span", "answer_from": "text", "paragraphs": "Other Income (Expense), Net\nInterest expense decreased $54.7 million during fiscal 2019 as compared to fiscal 2018, primarily due to elimination of the non-recourse residential financing obligations in connection with the sale of the Residential Lease Portfolio in November 2018, as well as the elimination of the sales-leaseback financing obligations in connection with the sale of the commercial sale-leaseback portfolio during the first and second quarters of fiscal 2019.\nInterest expense increased $17.7 million in fiscal 2018 as compared to fiscal 2017 primarily due to new debt and new commercial sale-leaseback arrangements.\nOther income increased by $119.4 million during fiscal 2019 as compared to fiscal 2018, primarily due to a $158.3 million gain on an equity investment with readily determinable fair value in fiscal 2019, as compared to a loss of $6.4 million in fiscal 2018. Additionally, gain on sale of equity investments during fiscal 2019 was $17.7 million, compared to $54.2 million in fiscal 2018.\nOther income increased by $143.0 million in fiscal 2018 as compared to fiscal 2017. The change is primarily due to a $54.2 million gain on the sale of our equity method investments in fiscal 2018, a $73.0 million impairment charge in fiscal 2017 in our 8point3 Energy Partners LP equity investment balance due to the adoption of ASC 606 which materially increased the investment balance and consequently, led to the recognition of an other-than-temporary impairment in the first quarter of fiscal 2017.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "7030e0a5e64490b1f47f9c695f68edde", "source_table_id": "4967072c92abc844ee63806035c6bd23"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:2414056711fcca15c9631460a4f0b729", "source": "tatqa", "table_key": "b33070bb52a2cfd91613472911127aed851b6c8a", "grid": {"TD": 1, "LD": 0, "nrows": 19, "ncols": 3, "section_rows": [], "cells": [["", "Years Ended December 31,", ""], ["", "2019", "2018"], ["Tax provision (benefit) at federal statutory rate", "$ (11.9)", "$ 38.3"], ["Permanent differences", "0.3", "1.5"], ["State tax, net of federal benefit", "(7.3)", "6.2"], ["Foreign rate differential", "1.4", "(0.9)"], ["Minority interest", "0.2", "(4.6)"], ["Executive and stock compensation", "2.5", "3.5"], ["Increase (decrease) in valuation allowance", "(7.6)", "(43.8)"], ["Transaction costs", "0.1", "1.5"], ["Tax credits generated/utilized", "(2.2)", "—"], ["Return to provision", "(6.0)", "15.6"], ["ASU 2017-11 adoption", "(1.3)", "—"], ["Goodwill impairment", "10.9", "—"], ["Gain/loss on sale or deconsolidation of a subsidiary", "—", "5.7"], ["Bargain purchase gain", "—", "(24.2)"], ["Other", "(1.8)", "3.6"], ["Warrant liability", "2.1", "—"], ["Income tax (benefit) expense", "$ (20.6)", "$ 2.4"]]}, "native_split": "test", "question": "What was the income tax benefit in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$20.6 million", "answer_type": "span", "answer_from": "table-text", "paragraphs": "16. Income Taxes\nThe provisions (benefits) for income taxes differed from the amount computed by applying the federal statutory income tax rate to income (loss) before income taxes due to the following items for the years ended December 31, 2019 and 2018 (in millions):\nThe income tax benefit as of December 31, 2019 is $20.6 million. The benefit was primarily driven by a net valuation allowance release of $37.4 million related to the Insurance segment partially offset by an impairment of goodwill which is not deductible for tax purposes. The Insurance segment is profitable in 2019 and in a three-year overall cumulative income position as of December 31, 2019.\nThe profitability is driven by current year income associated with favorable claims and reserve development relative to expected. Further, unrealized gains from the investment portfolio continued to grow in 2019.\nThe amount recorded as of December 31, 2018 primarily relates to separate state filings that do not have net operating losses available to offset income. In the third quarter of 2018, the Insurance segment acquired Humana’s long-term care business, Kanawha Insurance Company. The combined insurance entity generated a net operating loss for the year due to additional tax deductions related to increases in policy holder reserves. In addition, the bargain purchase gain is not taxable. This net operating loss was carried forward but had a valuation allowance. Additionally, the income tax expense generated from the sale of BeneVir in the second quarter of 2018 is offset by tax attributes for which a valuation allowance had been recorded. Therefore, there is no net income tax expense recorded in the income statement for the sale.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 5, "source_local_id": "2414056711fcca15c9631460a4f0b729", "source_table_id": "a15b8c598d0cb2aae4925e9045c4d9b4"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:fae632c334469b40e4d1172b0e009cfa", "source": "tatqa", "table_key": "447aee3ab90b001550df59cdf0dfd542a7d07597", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 3, "section_rows": [], "cells": [["", "As of", "As of"], ["", "December 31, 2019", "December 31, 2018"], ["Deferred tax assets:", "", ""], ["Reserves and accruals", "$62", "$45"], ["Research and development credits and other credits", "1,730", "1,635"], ["Net operating loss carry forward", "27,907", "25,733"], ["Stock based compensation", "8,402", "8,857"], ["Other", "11", "26"], ["Total deferred tax assets", "38,112", "36,296"], ["Valuation allowance", "(38,112)", "(36,296)"], ["Deferred tax assets after valuation allowance", "—", "—"], ["Total deferred tax liability", "—", "—"], ["Net deferred tax assets (liabilities)", "$—", "$—"]]}, "native_split": "test", "question": "What is the realization of the net deferred tax assets ultimately dependent upon?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "the generation of future taxable income during the periods in which those temporary differences become deductible.", "answer_type": "span", "answer_from": "text", "paragraphs": "In 2019 and 2018, we had pre-tax losses of $19,573 and $25,403 respectively, which are available for carry forward to offset future taxable income. We made determinations to provide full valuation allowances for our net deferred tax assets at the end of 2019 and 2018, including NOL carryforwards generated during the years, based on our evaluation of positive and negative evidence, including our history of operating losses and the uncertainty of generating future taxable income that would enable us to realize our deferred tax assets.\nDeferred tax assets (liabilities) consist of the following:\nIn assessing the realization of deferred tax assets, management considers whether it is more likely than not that a portion of the net deferred assets will not be realized. The ultimate realization of the net deferred tax assets is dependent upon the generation of future taxable income during the periods in which those temporary differences become deductible. Based on the available objective evidence, management believes it is more likely than not that the net deferred tax assets at December 31, 2019 will not be realizable. Accordingly, management has maintained a full valuation allowance against its net deferred tax assets at December 31, 2019. The net change in the total valuation allowance for the 12 months ended December 31, 2019 was an increase of $1,816.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "fae632c334469b40e4d1172b0e009cfa", "source_table_id": "8cd44786a6426d17faa6f76b0d645467"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:6489981220b4480ce47a90a6a1697720", "source": "tatqa", "table_key": "925c542ba9e296e8c9e8815bcce06b6a9eaac0fc", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2018", "2019"], ["Personnel-related items", "32,636", "45,318"], ["Deferred revenue", "28,255", "32,146"], ["Financing related items", "–", "3,912"], ["Current portion of lease liabilities", "–", "7,002"], ["Advanced payments from customers", "25,382", "47,601"], ["Supplier related items", "1,828", "2,175"], ["Marketing related items", "1,049", "1,365"], ["R&D projects", "1,209", "1,109"], ["Other", "8,634", "9,215"], ["Total accrued expenses and other payables", "98,993", "149,843"]]}, "native_split": "test", "question": "What does \"Other\" include in the table?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "accruals for VAT, other taxes and invoices to be received for goods and services", "answer_type": "span", "answer_from": "text", "paragraphs": "NOTE 15. ACCRUED EXPENSES AND OTHER PAYABLES\nAccrued expenses and other payables consist of the following:\nPersonnel-related items comprise accrued management bonuses, accrued vacation days, accrued wage tax, social securities and pension premiums. Deferred revenue consists of the revenue relating to the undelivered elements of the arrangements, see Note 21 for more information. This part of revenue is deferred at their relative selling prices until delivery of these elements. Other includes accruals for VAT, other taxes and invoices to be received for goods and services.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "6489981220b4480ce47a90a6a1697720", "source_table_id": "9aa587f522dd1d5bd2c0e6638699f52e"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:6b627f3347fa1873e27788f5ae13ac6a", "source": "tatqa", "table_key": "55a3ab34eeb0eea49d87996ed4f3ab1b284929f1", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Trade accounts receivable, net, current (1)", "$375,136", "$335,377"], ["Trade accounts receivable, net, noncurrent (2)", "26,496", "15,948"], ["", "$401,632", "$351,325"]]}, "native_split": "test", "question": "In which year was the Trade accounts receivable, net, current larger?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "2019", "answer_type": "span", "answer_from": "table", "paragraphs": "Trade Accounts Receivable\nThe Company’s trade accounts receivable, net, consisted of the following (in thousands):\n(1) Included in “Receivables, net” in the accompanying Consolidated Balance Sheets.\n(2) Included in “Deferred charges and other assets” in the accompanying Consolidated Balance Sheets.\nThe Company’s noncurrent trade accounts receivable result from (1) contracts with customers that include renewal provisions, and (2) contracts with customers under multi-year arrangements. For contracts that include renewal provisions, revenue is recognized up-front upon satisfaction of the associated performance obligations, but payments are received upon renewal. Renewals occur in bi-annual and annual increments over the associated expected contract term, the majority of which range from two to five years. The Company’s contracts with customers under multi-year arrangements generally have three-year terms and are invoiced annually at the beginning of each annual coverage period. The Company records a receivable related to revenue recognized under multi-year arrangements as the Company has an unconditional right to invoice and receive payment in the future related to these arrangements.\nWhere the timing of revenue recognition differs from the timing of invoicing and payment, the Company has determined that its contracts do not include a significant financing component. A substantial amount of the consideration promised by the customer under the contracts that include renewal provisions is variable, and the amount and timing of that consideration varies based on the occurrence or nonoccurrence of future events that are not substantially within the Company’s control. With respect to multi-year year arrangements, there is minimal difference between the consideration received and the cash selling price, any offered discounts are driven by volume, and the contracts are of short duration resulting in insignificant interest. Thus, the primary purpose of the invoicing terms on the multi-year arrangements is to provide the customer with a simplified and predictable way of purchasing certain products, not to provide financing or to receive financing from the Company’s customer.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 6, "source_local_id": "6b627f3347fa1873e27788f5ae13ac6a", "source_table_id": "aad7041cfc521e55841bb2b62200bad1"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:2974427dee28044990879e106b1d47a3", "source": "tatqa", "table_key": "cd499bf4d09de1d5ffc52e26075b89120397aac3", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 3, "section_rows": [], "cells": [["$ million", "2019", "2018"], ["Cash flow from operations", "124.9", "65.9"], ["Tax paid", "(5.6)", "(5.7)"], ["Net cash inflow from operating activities", "119.3", "60.2"], ["Interest received", "2.6", "1.3"], ["Net capital expenditure", "(11.9)", "(10.6)"], ["Payment of lease liabilities, principal and interest1", "(10.3)", "—"], ["Lease payments received from finance leases", "0.4", "—"], ["Free cash flow", "100.1", "50.9"]]}, "native_split": "test", "question": "What is the free cash flow in 2019?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "$100.1 million", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Financing and cash flow\nThe Group delivered very strong cash generation in 2019, driven by higher operating profit and effective working capital management. Changes in working capital, reflected within cash flow from operations, benefited from:\n• our continued focus on improving trade receivables collection; • a reduction in inventory levels due to a high level of shipments at the end of 2019; and • growth in payables, resulting from the increase in activity levels and emphasis on extending supplier payment terms.\nFree cash flow for 2019 almost doubled year-on-year coming in at $100.1 million, compared to $50.9 million in 2018, resulting in a free cash flow conversion which represented 123 per cent of adjusted earnings (2018 77 per cent).\nFree cash flow is set out below:\nNote\n1. Spirent adopted IFRS 16 on 1 January 2019; in prior periods operating lease payments were included within cash flow from operations.\nFree cash flow includes a net cash outflow in respect of exceptional items in 2018 and 2019 of $5.5 million (2018 $3.6 million in respect of exceptional items charged in 2017 and 2018).\nTax payments of $5.6 million made in 2019 were consistent with the prior year (2018 $5.7 million). Net capital expenditure of $11.9 million was also broadly consistent with the prior year (2018 $10.6 million), with the incremental spend of $1.3 million primarily related to investment in 5G. We continue to exercise careful management of capital investment to ensure efficient use of capital and maximise return on investment.\nFollowing the adoption of IFRS 16 on 1 January 2019, the payment of lease liabilities, both the principal and interest elements, are shown separately from net cash flow from operating activities. In previous periods they would have been reflected in that number. There is no overall impact in comparing free cash flow year-on-year.\nIn 2019, the final dividend for 2018 and an interim dividend for 2019 totalling $28.6 million were paid. This compared to total dividends of $54.8 million paid in 2018, including a special dividend of $29.9 million. In addition, 4.0 million shares were purchased and placed into the Employee Share Ownership Trust at a cost of $8.6 million (2018 1.5 million shares at a net cost of $2.5 million) and $1.9 million of cash consideration was paid to acquire the business of a navigation systems company based in the United Kingdom.\nFollowing these payments, cash and cash equivalents closed at $183.2 million at 31 December 2019, compared with $121.6 million at 31 December 2018. There continues to be no bank debt.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 12, "source_local_id": "2974427dee28044990879e106b1d47a3", "source_table_id": "902a9a9cf29ffa7b1434e0e604e0c8ea"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:40565b973fc2f80c0cf2737d11f14d2c", "source": "tatqa", "table_key": "a83aa4e885f2751b3b31bc51d405f8bc57cb84cc", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 3, "section_rows": [], "cells": [["", "Number of Shares", "Amount"], ["", "", "(U.S. $ in thousands)"], ["Details", "", ""], ["Balance as of June 30, 2017", "91,979,704", "$9,198"], ["Conversion of Class B ordinary shares", "5,861,707", "587"], ["Exercise of share options", "1,902,084", "190"], ["Issuance for settlement of RSUs", "5,253,809", "525"], ["Vesting of share options that were early exercised", "374,496", "37"], ["Balance as of June 30, 2018", "105,371,800", "10,537"], ["Conversion of Class B ordinary shares", "5,219,947", "522"], ["Exercise of share options", "1,496,875", "150"], ["Issuance for settlement of RSUs", "4,674,873", "467"], ["Vesting of share options that were early exercised", "510,071", "51"], ["Balance as of June 30, 2019", "117,273,566", "$11,727"]]}, "native_split": "test", "question": "What does the table show?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "Movements in Class A ordinary share capital", "answer_type": "span", "answer_from": "text", "paragraphs": "Movements in Class A ordinary share capital\nClass A shares as of June 30, 2019 and June 30, 2018 does not include 911,367 and 827,871 shares of restricted stock outstanding, respectively, that are subject to forfeiture or repurchase.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 2, "source_local_id": "40565b973fc2f80c0cf2737d11f14d2c", "source_table_id": "18fcffb6cdd39b9830c130a53cbcc4f2"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:02138be39f33e574bea5dceca9d0f034", "source": "tatqa", "table_key": "743f943f4cd6235ddcf4d0c1faa1e3af02586b74", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 7, "section_rows": [], "cells": [["For the year ended", "2019", "2019", "2018", "2018", "2017", "2017"], ["Revenue", "$23,406", "100%", "$30,391", "100%", "$20,322", "100%"], ["Cost of goods sold", "12,704", "54%", "12,500", "41%", "11,886", "58%"], ["Gross margin", "10,702", "46%", "17,891", "59%", "8,436", "42%"], ["Selling, general, and administrative", "836", "4%", "813", "3%", "743", "4%"], ["Research and development", "2,441", "10%", "2,141", "7%", "1,824", "9%"], ["Other operating (income) expense, net", "49", "—%", "(57)", "—%", "1", "—%"], ["Operating income", "7,376", "32%", "14,994", "49%", "5,868", "29%"], ["Interest income (expense), net", "77", "—%", "(222)", "(1)%", "(560)", "(3)%"], ["Other non-operating income (expense), net", "(405)", "(2)%", "(465)", "(2)%", "(112)", "(1)%"], ["Income tax (provision) benefit", "(693)", "(3)%", "(168)", "(1)%", "(114)", "(1)%"], ["Equity in net income (loss) of equity method investees", "3", "—%", "(1)", "—%", "8", "—%"], ["Net income attributable to noncontrolling interests", "(45)", "—%", "(3)", "—%", "(1)", "—%"], ["Net income attributable to Micron", "$6,313", "27%", "$14,135", "47%", "$5,089", "25%"]]}, "native_split": "test", "question": "What drove the increase in revenue for both DRAM and NAND from 2017 to 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "strong execution in delivering high-value products", "answer_type": "span", "answer_from": "text", "paragraphs": "Results of Operations\nConsolidated Results\nTotal Revenue\nTotal revenue for 2019 decreased 23% as compared to 2018 primarily due to pricing declines resulting from the challenging memory market environment in 2019. Sales of DRAM products for 2019 decreased 28% as compared to 2018 primarily due to declines in average selling prices of approximately 30% resulting from supply and demand imbalances, customer inventory corrections, and CPU shortages. Sales of NAND products for 2019 decreased 12% as compared to 2018 primarily due to declines in average selling prices in the mid-40% range resulting from supply and demand imbalances, which were partially offset by significant increases in sales volumes. In addition, demand for our NAND products was adversely affected by the transition from SATA SSDs to NVMe SSDs. The higher NAND sales volumes in 2019 were driven by increases in sales of high-value mobile managed NAND products as well as discrete NAND products enabled by our execution in ramping 64- and 96-layer TLC 3D NAND.\nTotal revenue for 2018 increased 50% as compared to 2017. Higher revenue in 2018 for both DRAM and NAND as compared to 2017 were driven by strong execution in delivering high-value products featuring our 1Xnm DRAM and 64-layer 3D NAND technologies combined with strong demand for products across our primary markets. Sales of DRAM products for 2018 increased 64% from 2017 primarily due to an increase in average selling prices of approximately 35% and an increase in sales volumes of approximately 20% as a result of strong market conditions, particularly for cloud, enterprise, mobile, and graphics markets, combined with increased sales into high-value markets. Sales of NAND products for 2018 increased 20% from 2017 despite declines in average selling prices primarily due to an increase in sales volumes of approximately 30% driven by increases in sales of high-value SSD and mobile managed NAND products enabled by strong demand and our execution in delivering 3D NAND products.\nOverall Gross Margin\nOur overall gross margin percentage decreased to 46% for 2019 from 59% for 2018 primarily due to declines in average selling prices partially offset by cost reductions resulting from strong execution in delivering products featuring advanced technologies and from continuous improvement initiatives to reduce production costs. Underutilization of IMFT assets adversely impacted our gross margin by a per-quarter average of approximately $100 million in 2019 and $65 million in 2018, and we anticipate the adverse impact of underutilization at IMFT to increase to approximately $150 million per quarter beginning in the first quarter of 2020.\nWe continue to evaluate planned technology node transitions, capital spending and re-use rates for NAND equipment. Based on our preliminary assessment, we anticipate changing the depreciable life of our NAND equipment from five to seven years beginning in the first quarter of 2020. We anticipate this change will reduce our depreciation expense included in cost of goods sold for the first quarter of 2020 by approximately $80 million, increasing to approximately $100 to $150 million per quarter for the remainder of 2020.\nOur overall gross margin percentage increased to 59% for 2018 from 42% for 2017 primarily due to favorable market conditions across key markets combined with strong execution in delivering products featuring advanced technologies, including 1Xnm DRAM and 64-layer 3D NAND, enabling manufacturing cost reductions. For 2018 as compared to 2017, pricing for DRAM products increased while manufacturing costs declined and, for NAND products, manufacturing cost reductions outpaced declines in average selling prices.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 9, "source_local_id": "02138be39f33e574bea5dceca9d0f034", "source_table_id": "c49bcea15896dba87eb972b0350e50d2"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:107f1d63d192600d57e2f2e2a1573e24", "source": "tatqa", "table_key": "e309c3824bf5c4985cb805896a2845ea29003df7", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "Dividend Paid per Ordinary Share", ""], ["Year Ended December 31,", "€", "US$"], ["2015", "1.15", "1.30 (1)"], ["2016", "1.25", "1.37 (1)"], ["2017", "1.40", "1.65 (1)"], ["2018", "1.50", "1.68 (1)"], ["2019 (proposed)", "1.58 (2)", "1.73 (2), (3)"]]}, "native_split": "test", "question": "What are the US$ values for 2015 to 2018 translated for?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "Translated for the convenience of the reader from euro into U.S. dollars at the Noon Buying Rate for converting euro into U.S. dollars on the dividend payment date.", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Annual Dividends Paid and Proposed\nThe following table sets forth in euro the annual dividends paid or proposed to be paid per ordinary share in respect of each of the years indicated. One SAP ADR currently represents one SAP SE ordinary share. Accordingly, the final dividend per ADR is equal to the dividend for one SAP SE ordinary share and is dependent on the euro/U.S. dollar exchange rate. The table does not reflect tax credits that may be available to German taxpayers who receive dividend payments. If you own our ordinary shares or ADRs and if you are a U.S. resident, refer to “Item 10. Additional Information — Taxation,” for further information.\n(1) Translated for the convenience of the reader from euro into U.S. dollars at the Noon Buying Rate for converting euro into U.S. dollars on the dividend payment date. The Depositary is required to convert any dividend payments received from SAP as promptly as practicable upon receipt.\n(2) Subject to approval at the Annual General Meeting of Shareholders of SAP SE currently scheduled to be held on May 15, 2020.\n(3) Translated for the convenience of the reader from euro into U.S. dollars at the Noon Buying Rate for converting euro into U.S. dollars on February 7, 2020 of US$1.0950 per €1.00. The dividend paid may differ due to changes in the exchange rate.\nThe amount of dividends paid on the ordinary shares depends on the amount of profits to be distributed by SAP SE, which depends in part upon our financial performance. In addition, the amount of dividends received by holders of ADRs may be affected by fluctuations in exchange rates (see “Item 3. Key Information — Exchange Rates”). The timing, declaration, amount and payment of any future dividend will depend upon our future earnings, capital needs and other relevant factors, in each case as proposed by the Executive Board and the Supervisory Board of SAP SE and approved by the Annual General Meeting of Shareholders.\nIn light of SAP’s strong financial performance and healthy balance sheet, the Supervisory Board of SAP SE approved, on November 4, 2019, the Executive Board’s plan for enhanced capital return in 2020. Under this new program, the Company intends to repurchase shares and/or issue a special dividend with a combined volume of €1.5 billion by December 31, 2020.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 7, "source_local_id": "107f1d63d192600d57e2f2e2a1573e24", "source_table_id": "504d01c4bb83fc6d346c56fee69948cc"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:743e1d8f4e38f97a17ab69892798a5b3", "source": "tatqa", "table_key": "91f40ab33590de5c6c6021fca9cf92de7e1b98bd", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["€ million", "2017/2018", "2018/2019"], ["Wages and salaries", "2,173", "2,264"], ["Social security expenses, expenses for post-employment benefits and related employee benefits", "552", "552"], ["thereof for post-employment benefits", "(43)", "(41)"], ["", "2,725", "2,816"]]}, "native_split": "test", "question": "What do wages and salaries include?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "expenses relating to restructuring measures and severance payments of €23 million (2017/18: €19 million)", "answer_type": "span", "answer_from": "table-text", "paragraphs": "17. Personnel expenses\nPersonnel expenses can be broken down as follows:\nWages and salaries include expenses relating to restructuring measures and severance payments of €23 million (2017/18: €19 million). The variable remuneration rose from €52 million in financial year 2017/18 to €81 million in financial year 2018/19. Wages and salaries also include expenses for long-term remuneration components totalling €7 million (2017/18: €16 million).", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 3, "source_local_id": "743e1d8f4e38f97a17ab69892798a5b3", "source_table_id": "f915e7aa1501c4c2f1c672d890ab1cee"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:bcb688fa9aa82e47f2499ea7c0c6f734", "source": "tatqa", "table_key": "c4d56953f8bd22a6ac5885505c6246ca777520a5", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "As of December 31,", ""], ["", "2019", "2018"], ["Projected benefit obligation", "$3,778", "$3,848"], ["Accumulated benefit obligation", "$2,999", "$3,028"], ["Fair value of plan assets", "$1,418", "$1,426"]]}, "native_split": "test", "question": "What was the Projected benefit obligation in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "3,848", "answer_type": "span", "answer_from": "table-text", "paragraphs": "NOTES TO CONSOLIDATED FINANCIAL STATEMENTS (in thousands, except for share and per share data)\nThe projected benefit obligation, accumulated benefit obligation and fair value of plan assets for those Pension Plans with accumulated benefit obligation in excess of fair value of plan assets is shown below:", "scale": "thousand", "scoring": "exact", "meta": {"paragraph_count": 2, "source_local_id": "bcb688fa9aa82e47f2499ea7c0c6f734", "source_table_id": "dc0d8f2313478f7e229f7f76985d90ee"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:c6ad108403b4cf68906ea27d857d764b", "source": "tatqa", "table_key": "070471222de9c68f8bb37f9423cd386bb58ee343", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 3, "section_rows": [], "cells": [["", "December 31,", ""], ["", "2019", "2018"], ["Trade receivables — billed", "$136.6", "$136.6"], ["Trade receivables — unbilled", "39.8", "37.0"], ["Trade receivables", "176.4", "173.6"], ["Allowance for doubtful accounts", "(1.3)", "(1.3)"], ["Trade receivables, net", "$175.1", "$172.3"]]}, "native_split": "test", "question": "What were the unbilled trade receivables in 2018?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "37.0", "answer_type": "span", "answer_from": "table-text", "paragraphs": "Trade Receivables, Net\nThe carrying amounts reported in the Consolidated Balance Sheets for Trade receivables, net approximate their fair value because of their short-term nature.\nA summary of Trade receivables, net of allowance for doubtful accounts is as follows (in millions):\nIn addition to the amounts above, we have unbilled receivables that we do not expect to collect within the next year included in Other non-current assets in our Consolidated Balance Sheets. Billings for these receivables are based on contractual terms. Refer to Note 11 — Other Non-Current Assets.", "scale": "million", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "c6ad108403b4cf68906ea27d857d764b", "source_table_id": "7bc6da25adaae100b2983a53464947c1"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:3969ae303c803d094c0ac845d81e65a5", "source": "tatqa", "table_key": "1294c5df8532bc0e12900d666515b096c0b2d30e", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["", "2019", "2018", "2017"], ["Supplemental cash flow information:", "", "", ""], ["Cash paid for interest", "$750.2", "$789.7", "$712.1"], ["Cash paid for income taxes (net of refunds of $11.2, $25.0 and $20.7, respectively)", "147.5", "163.9", "136.5"], ["Non-cash investing and financing activities:", "", "", ""], ["(Decrease) increase in accounts payable and accrued expenses for purchases of property and equipment and construction activities", "(21.0)", "8.3", "34.0"], ["Purchases of property and equipment under finance leases, perpetual easements and capital leases", "81.3", "57.8", "54.8"], ["Fair value of debt assumed through acquisitions", "329.8", "—", "—"], ["Acquisition of Commercialization Rights (1)", "—", "24.8", "—"], ["Conversion of third-party debt to equity", "—", "—", "48.2"], ["Debt financed acquisition of communication sites", "—", "54.2", "—"]]}, "native_split": "test", "question": "What was the Acquisition of Commercialization Rights related to?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "Related to the note extinguishment with TV Azteca, S.A. de C.V. in 2018.", "answer_type": "span", "answer_from": "table-text", "paragraphs": "AMERICAN TOWER CORPORATION AND SUBSIDIARIES NOTES TO CONSOLIDATED FINANCIAL STATEMENTS\n20. SUPPLEMENTAL CASH FLOW INFORMATION\nSupplemental cash flow information and non-cash investing and financing activities are as follows for the years ended December 31,:\n(1) Related to the note extinguishment with TV Azteca, S.A. de C.V. in 2018.", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "3969ae303c803d094c0ac845d81e65a5", "source_table_id": "dd72572fa20be30bb762c6d9646ca39d"}, "dataset": "tatqa", "subtype": "span"}
{"id": "tatqa:f42bc5c0d0e50325be339ba6c2b4679a", "source": "tatqa", "table_key": "5e9ecd2769eb24ae3abb8878925df17324a330cf", "grid": {"TD": 1, "LD": 0, "nrows": 5, "ncols": 3, "section_rows": [], "cells": [["", "As at December 31", "As at December 31"], ["(In millions of dollars)", "2019", "2018"], ["Wireless devices and accessories", "380", "399"], ["Other finished goods and merchandise", "80", "67"], ["Total inventories", "460", "466"]]}, "native_split": "test", "question": "How is lower of cost determined?", "source_prompt": "{table}\n{paragraphs}\n{question}", "answer_dp": "determined on a weighted average cost basis for Wireless devices and accessories and a first-in, first-out basis for other finished goods and merchandise", "answer_type": "span", "answer_from": "text", "paragraphs": "ACCOUNTING POLICY\nWe measure inventories, including wireless devices and merchandise for resale, at the lower of cost (determined on a weighted average cost basis for Wireless devices and accessories and a first-in, first-out basis for other finished goods and merchandise) and net realizable value. We reverse a previous writedown to net realizable value, not to exceed the original recognized cost, if the inventories later increase in value.\nEXPLANATORY INFORMATION\nCost of equipment sales and merchandise for resale includes $2,496 million of inventory costs for 2019 (2018 – $2,515 million).", "scale": "", "scoring": "exact", "meta": {"paragraph_count": 4, "source_local_id": "f42bc5c0d0e50325be339ba6c2b4679a", "source_table_id": "1748650ac0e54c64ee35cdf6e48b953e"}, "dataset": "tatqa", "subtype": "span"}
{"id": "hitab:hitab_33_totto3781-1_0_1", "source": "hitab", "table_key": "4e006d160660675414ece86dea1c6362ecac2ed9", "grid": {"TD": 2, "LD": 3, "nrows": 7, "ncols": 9, "section_rows": [], "cells": [["", "", "", "regular season record", "regular season record", "regular season record", "postseason record", "postseason record", "postseason record"], ["", "", "", "w", "l", "win %", "w", "l", "win %"], ["chicago white sox", "1979", "1986", "522", "510", "0.506", "1", "3", "0.25"], ["oakland athletics", "1986", "1995", "798", "673", "0.542", "19", "13", "0.594"], ["st. louis cardinals", "1996", "2011", "1408", "1182", "0.544", "50", "42", "0.543"], ["total", "", "", "2728", "2365", "0.536", "70", "58", "0.547"], ["reference:", "", "", "", "", "", "", "", ""]]}, "native_split": "test", "question": "what is the regular season record of tony la russa for team chicago white sox?", "source_prompt": "{table}\n{question}", "answer_dp": ["522", "510"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_33_totto3781-1_0_1", "source_table_id": "33_totto3781-1"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11561_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "what is the average amount of debt financing received for immigrant-owned firms and for firms with canadian-born owners, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["174.7", "182.5"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11561_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11557_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "for debt financing applications, what percent of the full approved amount was approved for firms with canadian-born owners and firms with immigrant owners, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["81.7", "76.9"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11557_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_920_5306_1", "source": "hitab", "table_key": "b657592cd496b02a7546cec2d2ade3cac82efc77", "grid": {"TD": 3, "LD": 2, "nrows": 39, "ncols": 8, "section_rows": [3, 15, 27], "cells": [["", "", "women", "women", "women", "men", "men", "men"], ["", "", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to", "", "from", "to"], ["physical assault", "", "", "", "", "", "", ""], ["physical assault", "newfoundland and labrador", "21.5", "18.5", "24.8", "26.3", "22.7", "30.3"], ["physical assault", "prince edward island", "30.3", "26", "35", "29.6", "24.5", "35.2"], ["physical assault", "nova scotia", "28", "25.3", "31", "39.7", "36.2", "43.4"], ["physical assault", "new brunswick", "23.9", "21.2", "26.8", "30.1", "26.9", "33.5"], ["physical assault", "quebec", "20.8", "19.2", "22.6", "26.1", "24.3", "28"], ["physical assault", "ontario", "26.6", "24.7", "28.6", "33.4", "31.3", "35.6"], ["physical assault", "manitoba", "28.6", "26", "31.3", "36.5", "33.6", "39.6"], ["physical assault", "saskatchewan", "25.6", "23.7", "27.7", "36.5", "33.9", "39.3"], ["physical assault", "alberta", "30.5", "28.2", "32.8", "39.1", "36.6", "41.5"], ["physical assault", "british columbia", "29.5", "27.4", "31.6", "38.7", "36.3", "41.2"], ["physical assault", "provinces", "26.1", "25.2", "27.1", "33.3", "32.2", "34.4"], ["sexual assault", "", "", "", "", "", "", ""], ["sexual assault", "newfoundland and labrador", "25.4", "22.3", "29", "8.2", "6.1", "10.9"], ["sexual assault", "prince edward island", "34.6", "30.2", "39", "6.1", "4", "9.3"], ["sexual assault", "nova scotia", "33.1", "30.3", "36", "9.6", "7.8", "11.8"], ["sexual assault", "new brunswick", "28.5", "25.6", "32", "9.8", "7.8", "12.3"], ["sexual assault", "quebec", "24.5", "22.8", "26", "5.7", "4.9", "6.6"], ["sexual assault", "ontario", "29.8", "27.9", "32", "8.2", "7.1", "9.4"], ["sexual assault", "manitoba", "31.7", "29.2", "34", "8.9", "7.3", "10.9"], ["sexual assault", "saskatchewan", "30.9", "28.8", "33", "7.9", "6.5", "9.5"], ["sexual assault", "alberta", "35.3", "32.9", "38", "9.4", "8", "11"], ["sexual assault", "british columbia", "36.5", "34.4", "39", "11.1", "9.6", "12.7"], ["sexual assault", "provinces", "30.2", "29.3", "31", "8.2", "7.7", "8.8"], ["total violent victimization", "", "", "", "", "", "", ""], ["total violent victimization", "newfoundland and labrador", "32.3", "29", "35.8", "28.4", "24.7", "32.3"], ["total violent victimization", "prince edward island", "42.9", "38.3", "47.5", "30.9", "25.8", "36.6"], ["total violent victimization", "nova scotia", "41.6", "38.6", "44.7", "41.9", "38.3", "45.6"], ["total violent victimization", "new brunswick", "37.8", "34.6", "41", "32.5", "29.3", "35.9"], ["total violent victimization", "quebec", "31.7", "29.9", "33.6", "28.2", "26.3", "30.1"], ["total violent victimization", "ontario", "38.9", "36.9", "41", "35.5", "33.3", "37.8"], ["total violent victimization", "manitoba", "41.5", "38.8", "44.2", "39.1", "36.1", "42.2"], ["total violent victimization", "saskatchewan", "38.4", "36.2", "40.7", "37.9", "35.2", "40.6"], ["total violent victimization", "alberta", "44.1", "41.6", "46.6", "40.7", "38.3", "43.2"], ["total violent victimization", "british columbia", "44.5", "42.3", "46.8", "40.9", "38.5", "43.4"], ["total violent victimization", "provinces", "38.7", "37.7", "39.7", "35.3", "34.3", "36.4"]]}, "native_split": "test", "question": "among the provinces, men who were 15 or older in which three provinces were more likely to report experiencing physical assault than other provinces and the provincial average overall in 2018?", "source_prompt": "{table}\n{question}", "answer_dp": ["nova scotia", "alberta", "british columbia"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_920_5306_1", "source_table_id": "920"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1795_10105_1", "source": "hitab", "table_key": "e1bc438abddb2f1872aaecdbb881ffbcd21475a8", "grid": {"TD": 1, "LD": 2, "nrows": 32, "ncols": 17, "section_rows": [], "cells": [["", "", "n.l", "p.e.i", "n.s", "n.b", "que", "ont", "man", "sask", "alta", "b.c", "y.t", "n.w.t", "nvt", "canada", "difference in canada numberfrom previous year"], ["number of victims", "1985", "5", "1", "26", "14", "219", "193", "26", "28", "63", "113", "6", "10", "", "704", ""], ["number of victims", "1986", "4", "0", "15", "12", "155", "139", "47", "26", "64", "88", "3", "14", "", "567", "-137"], ["number of victims", "1987", "5", "0", "14", "20", "174", "204", "44", "30", "73", "78", "0", "2", "", "644", "77"], ["number of victims", "1988", "7", "1", "11", "8", "154", "186", "30", "23", "66", "80", "1", "8", "", "575", "-69"], ["number of victims", "1989", "5", "1", "16", "19", "213", "175", "42", "22", "67", "85", "2", "7", "", "654", "79"], ["number of victims", "1990", "0", "1", "8", "11", "184", "182", "38", "36", "74", "110", "1", "12", "", "657", "3"], ["number of victims", "1991", "10", "2", "21", "17", "180", "246", "42", "21", "83", "129", "0", "3", "", "754", "97"], ["number of victims", "1992", "2", "0", "21", "11", "166", "243", "29", "32", "92", "122", "2", "13", "", "733", "-21"], ["number of victims", "1993", "7", "2", "19", "11", "159", "192", "31", "30", "49", "119", "0", "7", "", "626", "-107"], ["number of victims", "1994", "4", "1", "19", "14", "126", "193", "29", "24", "66", "113", "3", "4", "", "596", "-30"], ["number of victims", "1995", "5", "1", "17", "13", "135", "181", "26", "21", "60", "121", "4", "3", "", "587", "-9"], ["number of victims", "1996", "7", "1", "18", "9", "154", "188", "45", "32", "51", "125", "0", "4", "", "634", "47"], ["number of victims", "1997", "7", "0", "24", "8", "131", "179", "31", "25", "61", "116", "1", "3", "", "586", "-48"], ["number of victims", "1998", "7", "1", "24", "5", "137", "157", "33", "33", "64", "90", "3", "5", "", "559", "-27"], ["number of victims", "1999", "2", "1", "14", "9", "137", "162", "26", "13", "61", "110", "1", "1", "2", "539", "-20"], ["number of victims", "2000", "6", "3", "16", "10", "147", "158", "30", "26", "59", "85", "2", "1", "3", "546", "7"], ["number of victims", "2001", "1", "2", "9", "8", "140", "171", "34", "26", "69", "85", "1", "4", "3", "553", "7"], ["number of victims", "2002", "2", "1", "10", "9", "117", "178", "36", "27", "70", "126", "0", "4", "2", "582", "29"], ["number of victims", "2003", "5", "1", "8", "8", "101", "178", "44", "41", "63", "94", "1", "4", "3", "551", "-31"], ["number of victims", "2004", "2", "0", "14", "7", "111", "188", "50", "39", "86", "113", "7", "4", "4", "625", "74"], ["number of victims", "2005", "11", "0", "21", "10", "100", "219", "49", "42", "108", "101", "1", "0", "2", "664", "39"], ["number of victims", "2006", "7", "1", "16", "7", "93", "195", "40", "42", "95", "109", "0", "0", "2", "607", "-57"], ["number of victims", "2007", "3", "0", "13", "8", "91", "202", "62", "30", "88", "87", "2", "2", "7", "595", "-12"], ["number of victims", "2008", "5", "2", "12", "3", "92", "176", "54", "30", "110", "117", "3", "3", "4", "611", "16"], ["number of victims", "2009", "1", "0", "15", "12", "87", "179", "57", "36", "96", "117", "2", "2", "6", "610", "-1"], ["number of victims", "2010", "4", "0", "22", "9", "85", "189", "45", "33", "76", "83", "1", "1", "6", "554", "-56"], ["number of victims", "2011", "4", "1", "22", "8", "105", "163", "53", "38", "113", "88", "0", "3", "7", "605", "51"], ["number of victims", "2012", "3", "0", "17", "6", "108", "163", "54", "29", "87", "71", "0", "5", "5", "548", "-57"], ["number of victims", "2013", "7", "1", "13", "7", "70", "168", "51", "31", "81", "74", "0", "2", "4", "509", "-39"], ["number of victims", "2014", "2", "3", "6", "10", "70", "156", "45", "24", "106", "89", "3", "3", "4", "521", "12"], ["number of victims", "2015", "3", "1", "12", "11", "77", "174", "47", "43", "133", "95", "1", "5", "2", "604", "83"]]}, "native_split": "test", "question": "in 2015,what were the increased number of homicides in alberta, saskatchewan and ontario respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["27", "19", "18"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1795_10105_1", "source_table_id": "1795"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2344_12591_1", "source": "hitab", "table_key": "2b74353590b63fe58204c52e82488e9cf9c12638", "grid": {"TD": 2, "LD": 3, "nrows": 12, "ncols": 11, "section_rows": [2, 7], "cells": [["", "", "", "women", "women", "women", "women", "men", "men", "men", "men"], ["", "", "", "bottom 90%", "next 5%", "next 4%", "top 1%", "bottom 90%", "next 5%", "next 4%", "top 1%"], ["percent", "distribution of hours worked", "", "", "", "", "", "", "", "", ""], ["percent", "distribution of hours worked", "less than 20", "18.9", "8.1", "8.8", "10.1", "11.7", "4.9", "5.2", "5.4"], ["percent", "distribution of hours worked", "from 20 to 39", "40.5", "27.8", "22", "15.9", "23.2", "17", "13.1", "10.5"], ["percent", "distribution of hours worked", "from 40 to 49", "34.7", "48.5", "46.4", "40.2", "50.8", "54.6", "50.8", "42.5"], ["percent", "distribution of hours worked", "50 and over", "6", "15.6", "22.8", "33.8", "14.4", "23.6", "31", "41.6"], ["percent", "distribution of hours worked for core-aged workers", "", "", "", "", "", "", "", "", ""], ["percent", "distribution of hours worked for core-aged workers", "less than 20", "14.1", "6.8", "7.5", "7.6", "6.9", "3.6", "3.6", "3.1"], ["percent", "distribution of hours worked for core-aged workers", "from 20 to 39", "40.2", "25.8", "19.9", "14", "21.1", "15", "10.5", "6.8"], ["percent", "distribution of hours worked for core-aged workers", "from 40 to 49", "39", "51.1", "48.9", "42.7", "56.2", "56.6", "52.7", "43.8"], ["percent", "distribution of hours worked for core-aged workers", "50 and over", "6.6", "16.2", "23.7", "35.7", "15.8", "24.9", "33.3", "46.3"]]}, "native_split": "test", "question": "for workers in the top 1%,what were the percentages of women spent over 50 hours on paid work in the week prior to census day and their male counterparts respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["33.8", "41.6"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2344_12591_1", "source_table_id": "2344"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1979_11027_1", "source": "hitab", "table_key": "6501832b9c4be97a3c6f6d2cd31de1ded430b644", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 11, "section_rows": [], "cells": [["", "", "average sector output share", "average sector output share", "average sector output share", "average foreign-controlled market share", "average foreign-controlled market share", "average foreign-controlled market share", "percentage change in foreign control", "percentage change in foreign control", "percentage change in foreign control"], ["", "", "1973 to 1975", "1997 to 1999", "2009 to 2011", "1973 to 1975", "1997 to 1999", "2009 to 2011", "between the 1973-to-1975 and 1997-to-1999 periods", "between the 1997-to-1999 and 2009-to-2011 periods", "between the 1973-to-1975 and 2009-to-2011 periods"], ["percent", "food, beverage and tobacco", "19.6", "13.7", "17.7", "35.8", "46.7", "32.8", "30.5", "-29.9", "-8.5"], ["percent", "natural-resources-based", "15.5", "14.7", "26.3", "57.9", "40.8", "48.4", "-29.5", "18.7", "-16.3"], ["percent", "labour-intensive", "14.4", "10.1", "8.8", "34.6", "29.6", "21.4", "-14.4", "-27.7", "-38.1"], ["percent", "scale-based", "34.8", "41.8", "29.1", "57", "61.1", "58", "7.2", "-5.2", "1.6"], ["percent", "product-differentiated", "8.6", "9.7", "9", "56.2", "47.9", "34", "-14.7", "-29", "-39.5"], ["percent", "science-based", "7", "10", "9.1", "72.6", "50.8", "46.6", "-30", "-8.2", "-35.8"], ["percent", "manufacturing", "100", "100", "100", "50.8", "50.7", "44.6", "-0.2", "-12.1", "-12.3"]]}, "native_split": "test", "question": "what were the shares of manufacturing output of scale-based industries between 1973 to 1975 and 1997 to 1999 respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["34.8", "41.8"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1979_11027_1", "source_table_id": "1979"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_294_totto33680-2_0_1", "source": "hitab", "table_key": "669f80864952e88cfbd54740b7a78df4a3951f2c", "grid": {"TD": 2, "LD": 4, "nrows": 33, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "total", "total", "scottish second division", "scottish second division", "scottish cup", "scottish cup", "league cup", "league cup", "challenge cup", "challenge cup"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["none", "gk", "scotland", "youssef bejaoui", "7", "0", "5+0", "0", "0+0", "0", "1+0", "0", "1+0", "0"], ["none", "gk", "england", "thomas flynn", "32", "0", "30+0", "0", "2+0", "0", "0+0", "0", "0+0", "0"], ["none", "gk", "scotland", "lee wilson", "1", "0", "1+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "kenny adamson", "35", "1", "30+1", "1", "2+0", "0", "1+0", "0", "1+0", "0"], ["none", "df", "scotland", "john armstrong", "35", "1", "33+1", "1", "1+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "steven bennett", "0", "0", "0+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "dean brett", "16", "0", "14+0", "0", "0+0", "0", "1+0", "0", "1+0", "0"], ["none", "df", "scotland", "paul byrne", "2", "0", "0+2", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "dene droudge", "0", "0", "0+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "scott linton", "37", "5", "30+3", "4", "2+0", "1", "0+1", "0", "1+0", "0"], ["none", "df", "scotland", "joe mbu", "34", "2", "30+0", "2", "2+0", "0", "1+0", "0", "1+0", "0"], ["none", "df", "scotland", "thomas o'brien", "16", "1", "11+4", "1", "1+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "england", "david cowan", "17", "0", "15+0", "0", "2+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "gary fisher", "10", "0", "9+1", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "colin cameron", "26", "1", "20+2", "1", "2+0", "0", "1+0", "0", "1+0", "0"], ["none", "mf", "scotland", "danny mackay", "0", "0", "0+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "england", "lee makel", "3", "0", "1+2", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "kyle miller", "6", "0", "2+3", "0", "0+0", "0", "0+0", "0", "0+1", "0"], ["none", "mf", "scotland", "jordan morton", "20", "6", "12+6", "6", "0+0", "0", "1+0", "0", "1+0", "0"], ["none", "mf", "scotland", "mark ramsay", "28", "8", "19+5", "6", "2+0", "0", "1+0", "2", "1+0", "0"], ["none", "mf", "scotland", "jon robertson", "39", "7", "35+0", "5", "2+0", "2", "1+0", "0", "1+0", "0"], ["none", "mf", "scotland", "craig winter", "12", "0", "3+6", "0", "0+1", "0", "1+0", "0", "1+0", "0"], ["none", "mf", "scotland", "lewis milne", "8", "0", "3+4", "0", "0+0", "0", "0+0", "0", "0+1", "0"], ["none", "mf", "scotland", "dean brett", "3", "0", "2+1", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "lewis coult", "36", "15", "20+12", "13", "0+2", "1", "1+0", "0", "0+1", "1"], ["none", "fw", "scotland", "john ferguson", "1", "0", "1+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "marc mckenzie", "39", "18", "31+5", "18", "1+1", "0", "0+1", "0", "0+0", "0"], ["none", "fw", "scotland", "greg stewart", "33", "7", "17+12", "6", "2+0", "1", "1+0", "0", "1+0", "0"], ["none", "fw", "scotland", "kal naismith", "10", "2", "7+2", "2", "1+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "derek lyle", "18", "1", "12+6", "1", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "liam cusack", "13", "0", "7+6", "0", "0+0", "0", "0+0", "0", "0+0", "0"]]}, "native_split": "test", "question": "which cup games did the cowdenbeath f.c. appear for?", "source_prompt": "{table}\n{question}", "answer_dp": ["scottish cup", "challenge cup", "league cup"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_294_totto33680-2_0_1", "source_table_id": "294_totto33680-2"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1963_10959_1", "source": "hitab", "table_key": "4951a8d96568ec197e38077b17fd71da6875fa5d", "grid": {"TD": 2, "LD": 1, "nrows": 16, "ncols": 8, "section_rows": [], "cells": [["", "total crime severity index", "total crime severity index", "total crime severity index", "violent crime severity index", "violent crime severity index", "non-violent crime severity index", "non-violent crime severity index"], ["", "index", "percent change 2016 to 2017", "percent change 2007 to 2017", "index", "percent change 2016 to 2017", "index", "percent change 2016 to 2017"], ["newfoundland and labrador", "63", "-9", "-16", "69.9", "-5", "60.4", "-11"], ["prince edward island", "44.9", "-7", "-30", "38.9", "7", "47", "-11"], ["nova scotia", "65.9", "6", "-28", "78.2", "11", "61.3", "4"], ["new brunswick", "69", "11", "-2", "69.6", "5", "68.7", "14"], ["quebec", "57.1", "2", "-33", "71.8", "7", "51.7", "0"], ["ontario", "55.4", "5", "-26", "68.7", "7", "50.5", "4"], ["manitoba", "118.1", "3", "-22", "159.8", "5", "102.9", "2"], ["saskatchewan", "140.5", "-6", "-15", "141.2", "-5", "139.9", "-6"], ["alberta", "110.1", "5", "-4", "96.3", "5", "114.8", "5"], ["british columbia", "88.9", "-5", "-33", "75.5", "0", "93.6", "-7"], ["yukon", "189.3", "1", "2", "261.6", "9", "162.8", "-4"], ["northwest territories", "303.8", "2", "-10", "361.6", "7", "282.3", "0"], ["nunavut", "297.6", "1", "-6", "472.3", "10", "234.1", "-4"], ["canada", "72.9", "2", "-24", "80.3", "5", "70", "0"]]}, "native_split": "test", "question": "which province reported the largest decrease in the nvcsi in 2017?", "source_prompt": "{table}\n{question}", "answer_dp": ["prince edward island", "newfoundland and labrador"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_1963_10959_1", "source_table_id": "1963"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_358_2005_1", "source": "hitab", "table_key": "eb04000e26e81449a11d75f3e285db4faf491213", "grid": {"TD": 3, "LD": 1, "nrows": 53, "ncols": 10, "section_rows": [], "cells": [["", "2016 population", "police officers", "police officers", "police officers", "police officers", "hires", "departures", "net gain or loss: hirings and departures", "police officers eligible to retire"], ["", "number", "2017 number of police officers", "2017 police officers per 100,000 population", "percentage change from previous year", "2017 percentage of female officers", "number", "number", "number", "percent"], ["", "", "number", "rate", "percent", "percent", "", "", "", ""], ["toronto, ont", "2876095", "5190", "180", "-4.9", "19.1", "26", "171", "-145", "7.1"], ["montreal, que", "2014221", "4596", "228", "-0.9", "32.1", "180", "220", "-40", "16.3"], ["peel region (mississauga/brampton), ont", "1404431", "1973", "140", "-2", "18.3", "100", "74", "26", "4.5"], ["calgary, alta", "1318817", "2215", "168", "-0.4", "18.5", "128", "55", "73", "0"], ["york region (markham/vaughan),ont", "1157419", "1586", "137", "-2.4", "19", "52", "39", "13", "5.3"], ["ottawa, ont", "973481", "1242", "128", "-1.5", "22.6", "62", "45", "17", "5.8"], ["edmonton, alta", "969068", "1775", "183", "-0.2", "18.9", "134", "65", "69", "8.5"], ["winnipeg, man", "735552", "1409", "192", "-2.6", "15.6", "2", "43", "-41", "25.8"], ["durham region (oshawa/whitby/ajax), ont", "671839", "854", "127", "-2.3", "19.8", "36", "26", "10", "2.7"], ["vancouver, b.c", "670718", "1313", "196", "0", "25.3", "73", "34", "39", "8.4"], ["quebec, que", "575398", "757", "132", "-2.3", "27.2", "0", "6", "-6", "2.6"], ["halton region (oakville/burlington), ont", "569591", "686", "120", "-2.5", "20.7", "33", "23", "10", "2.2"], ["hamilton, ont", "561022", "835", "149", "-1.7", "23.8", "21", "26", "-5", "15.7"], ["waterloo region (kitchener), ont", "548936", "776", "141", "-0.1", "21.6", "33", "22", "11", "0.6"], ["surrey, b.c", "514522", "732", "142", "-1.3", "18.4", "72", "19", "53", "5.9"], ["niagara region (st. catharines), ont", "453817", "706", "156", "-0.8", "16.6", "22", "18", "4", "6.8"], ["laval, que", "429413", "611", "142", "0.9", "30", "11", "15", "-4", "12.3"], ["longueuil, que", "423525", "568", "134", "-0.4", "34.7", "10", "12", "-2", "3.5"], ["london, ont", "397493", "605", "152", "2.5", "20.3", "38", "29", "9", "3.6"], ["gatineau, que", "281392", "393", "140", "-1.7", "22.6", "13", "15", "-2", "4.3"], ["saskatoon, sask", "266064", "460", "173", "-1.4", "23", "17", "14", "3", "10.7"], ["burnaby, b.c", "246987", "290", "117", "1.5", "23.4", "31", "7", "24", "7.6"], ["halifax, n.s", "238467", "531", "223", "1.7", "19", "21", "17", "4", "2.3"], ["regina, sask", "223637", "397", "178", "-1.2", "24.9", "9", "11", "-2", "2.5"], ["windsor, ont", "221862", "428", "193", "-1.2", "15.9", "24", "17", "7", "4"], ["richmond, b.c", "209738", "206", "98", "1.3", "18.4", "30", "6", "24", "7.3"], ["st. john's, n.l", "207695", "341", "164", "-1.2", "28.7", "15", "16", "-1", "24"], ["richelieu saint-laurent, que", "205564", "212", "103", "-0.8", "23.1", "0", "12", "-12", "0"], ["greater sudbury, ont", "165154", "264", "160", "0", "17.4", "6", "9", "-3", "4.5"], ["sherbrooke, que", "164538", "200", "122", "-2.6", "23.5", "1", "4", "-3", "1.5"], ["barrie, ont", "150443", "239", "159", "-0.6", "15.9", "0", "2", "-2", "2.5"], ["saguenay, que", "147543", "178", "121", "0.2", "24.2", "0", "9", "-9", "3.4"], ["abbotsford, b.c", "146498", "203", "139", "-5.6", "18.2", "9", "8", "1", "3"], ["levis, que", "144918", "155", "107", "0.4", "21.3", "8", "7", "1", "10.3"], ["terrebonne, que", "139818", "169", "121", "0.7", "27.2", "19", "2", "17", "3"], ["coquitlam, b.c", "139055", "163", "117", "-3.9", "26.4", "11", "8", "3", "8.6"], ["trois-rivieres, que", "136168", "157", "115", "-0.5", "19.7", "4", "4", "0", "2.5"], ["guelph, ont", "132350", "194", "147", "-0.8", "22.7", "2", "9", "-7", "7.7"], ["kingston, ont", "131746", "203", "154", "-0.1", "18.7", "3", "4", "-1", "3.4"], ["kelowna, b.c", "129512", "173", "134", "-4.3", "31.8", "9", "10", "-1", "9.8"], ["codiac region (moncton), n.b", "118122", "134", "113", "-9.8", "18.7", "6", "14", "-8", "17.2"], ["saanich, b.c", "117285", "158", "135", "-1.2", "24.7", "7", "8", "-1", "5.1"], ["thunder bay, ont", "115740", "228", "197", "-1.3", "16.7", "6", "5", "1", "3.5"], ["langley township, b.c", "114940", "139", "121", "-1.6", "25.2", "13", "3", "10", "8.6"], ["delta, b.c", "110730", "168", "152", "6.6", "22.6", "14", "7", "7", "3"], ["roussillon region, que", "105835", "115", "109", "-1", "25.2", "0", "2", "-2", "1.7"], ["chatham-kent, ont", "104859", "155", "148", "-2.3", "13.5", "3", "2", "1", "10.3"], ["victoria, b.c", "104777", "244", "233", "-0.9", "25.4", "7", "6", "1", "6.1"], ["red deer, alta", "103690", "172", "166", "3.7", "22.7", "11", "6", "5", "5.8"], ["brantford, ont", "100791", "173", "172", "0.1", "15", "8", "4", "4", "0"]]}, "native_split": "test", "question": "among these 50 police services, what were the 10 ones with the highest rates of police strength in 2017?", "source_prompt": "{table}\n{question}", "answer_dp": ["victoria, b.c", "montreal, que", "halifax, n.s", "thunder bay, ont", "vancouver, b.c", "windsor, ont", "winnipeg, man", "edmonton, alta", "toronto, ont", "regina, sask"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_358_2005_1", "source_table_id": "358"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2701_14614_1", "source": "hitab", "table_key": "60fb8e84a913d349acf55b312b3476172bf08aa4", "grid": {"TD": 1, "LD": 3, "nrows": 54, "ncols": 7, "section_rows": [3, 6, 12, 15, 20, 24, 27, 33, 38, 41, 47, 51], "cells": [["", "", "", "neither cyberbullied nor cyberstalked", "cyberbullied, but not cyberstalked", "cyberstalked, but not cyberbullied", "cyberbullied and cyberstalked"], ["percent", "all 15-to-29 year-olds", "", "83.2", "6", "5.6", "5.2"], ["percent", "sociodemographic", "", "", "", "", ""], ["percent", "sexual orientation", "", "", "", "", ""], ["percent", "sexual orientation", "heterosexual", "84.6", "5", "5.6", "4.8"], ["percent", "sexual orientation", "homosexual/bi-sexual", "66", "12.2", "9.6", "12.3"], ["percent", "age group", "", "", "", "", ""], ["percent", "age group", "15 to 17", "81", "9.3", "5", "4.8"], ["percent", "age group", "18 to 20", "80.5", "7.8", "5.6", "6.1"], ["percent", "age group", "21 to 23", "84", "5.1", "6.1", "4.8"], ["percent", "age group", "24 to 26", "84.5", "5", "5.7", "4.8"], ["percent", "age group", "27 to 29", "85.4", "3.5", "5.6", "5.5"], ["percent", "gender", "", "", "", "", ""], ["percent", "gender", "men", "85.2", "6.1", "4.7", "4.1"], ["percent", "gender", "women", "81.1", "6", "6.6", "6.4"], ["percent", "province/region of residence", "", "", "", "", ""], ["percent", "province/region of residence", "atlantic", "82.1", "6.8", "5.2", "5.9"], ["percent", "province/region of residence", "quebec", "86.6", "5.9", "4.1", "3.4"], ["percent", "province/region of residence", "ontario", "81", "7", "6", "6.1"], ["percent", "province/region of residence", "west", "83.7", "4.9", "6.2", "5.2"], ["percent", "population size of community", "", "", "", "", ""], ["percent", "population size of community", "rural", "84", "6.6", "3.7", "5.8"], ["percent", "population size of community", "population centre", "82.9", "5.9", "6.1", "5.1"], ["percent", "family, family background", "", "", "", "", ""], ["percent", "marital status", "", "", "", "", ""], ["percent", "marital status", "currently married or ever-married/common-law", "88.8", "4", "4.1", "3.2"], ["percent", "marital status", "single/never-married", "81.7", "6.6", "6", "5.7"], ["percent", "parental education", "", "", "", "", ""], ["percent", "parental education", "both have high school or less", "81.1", "6.7", "7.1", "5.1"], ["percent", "parental education", "at least one has postsecondary education", "84.6", "5.3", "5", "5"], ["percent", "parental education", "both have postsecondary education", "82.5", "6.2", "5.8", "5.5"], ["percent", "parental education", "do not know either parent's education level", "84.4", "6", "4.5", "5.1"], ["percent", "education/main activity/household income", "", "", "", "", ""], ["percent", "highest level of education", "", "", "", "", ""], ["percent", "highest level of education", "less than high school", "81.2", "9.1", "4.5", "5.3"], ["percent", "highest level of education", "high school", "81.3", "5.7", "7", "6"], ["percent", "highest level of education", "postsecondary - non-university", "86.7", "4.4", "5", "4"], ["percent", "highest level of education", "postsecondary - university", "84.3", "5.2", "5.2", "5.3"], ["percent", "main activity in the last year", "", "", "", "", ""], ["percent", "main activity in the last year", "non-student", "84.5", "4.9", "5.6", "4.9"], ["percent", "main activity in the last year", "student", "81.7", "7.2", "5.6", "5.5"], ["percent", "household income", "", "", "", "", ""], ["percent", "household income", "less than $40,000", "75.6", "6.3", "11.1", "7.1"], ["percent", "household income", "$40,000 to $79,999", "83.4", "7.1", "5.2", "4.3"], ["percent", "household income", "$80,000 or more", "85.3", "5", "5.1", "4.6"], ["percent", "household income", "not stated", "82.6", "6.3", "5.4", "5.6"], ["percent", "cultural/ethnic", "", "", "", "", ""], ["percent", "religious affiliation", "", "", "", "", ""], ["percent", "religious affiliation", "none", "80.6", "6.2", "6", "7.2"], ["percent", "religious affiliation", "christian", "84.4", "6.3", "5.2", "4.2"], ["percent", "religious affiliation", "non-christian", "83.8", "4.2", "7.4", "4.6"], ["percent", "visible minority", "", "", "", "", ""], ["percent", "visible minority", "no", "82.2", "6.5", "5.6", "5.8"], ["percent", "visible minority", "yes", "86.1", "4.8", "5.4", "3.7"]]}, "native_split": "test", "question": "what were the percentages of the single/never-married population and those who had ever been (or were currently) married or living common-law who had been cyberstalked?", "source_prompt": "{table}\n{question}", "answer_dp": ["6", "4.1"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2701_14614_1", "source_table_id": "2701"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1820_10275_1", "source": "hitab", "table_key": "00db50cf39fb75658ceb7d2c4148ee1d490165d8", "grid": {"TD": 3, "LD": 1, "nrows": 11, "ncols": 7, "section_rows": [], "cells": [["", "participation rate", "participation rate", "participation rate", "average time spent", "average time spent", "average time spent"], ["", "excellent or very good", "good", "fair or poor", "excellent or very good", "good", "fair or poor"], ["", "percent", "percent", "percent", "hours", "hours", "hours"], ["doing unpaid household work", "89", "87", "84", "3.4", "3.2", "2.9"], ["engaging in civic, religious and organizational activities", "10", "9", "5", "2.3", "1.9", "2.1"], ["socializing and communicating", "45", "41", "37", "2.3", "2.2", "2.4"], ["engaging in leisure and physical activities", "45", "39", "36", "2.2", "2.1", "2.3"], ["using technology", "32", "27", "25", "1.9", "2", "2.3"], ["watching television and videos", "82", "88", "85", "3.7", "4.2", "4.9"], ["reading", "42", "37", "32", "2", "2", "2"], ["sleeping", "100", "100", "100", "8.9", "9.1", "9.5"]]}, "native_split": "test", "question": "what were the average time spent on unpaid work for those who reported fair or poor health and for those in excellent or very good health respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["2.9", "3.4"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1820_10275_1", "source_table_id": "1820"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2400_12842_1", "source": "hitab", "table_key": "93ce5ef6ba57acfc32bf9ebffa07e9042cf8c303", "grid": {"TD": 2, "LD": 2, "nrows": 8, "ncols": 7, "section_rows": [], "cells": [["", "", "sample size", "classification", "classification", "classification", "classification"], ["", "", "", "enumerated individuals", "missed individuals", "out of scope individuals", "non-respondents"], ["number", "2006 census", "54772", "47854", "3152", "2343", "1423"], ["number", "missed individuals from 2006", "5431", "3787", "737", "452", "455"], ["number", "births", "3619", "3223", "226", "55", "115"], ["number", "immigrants", "2548", "1900", "332", "110", "206"], ["number", "non-permanent residents", "1470", "670", "298", "82", "420"], ["number", "health insurance files", "76711", "74889", "901", "591", "330"]]}, "native_split": "test", "question": "how many respondents do the immigrant and npr frames comprise respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["2548", "1470"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2400_12842_1", "source_table_id": "2400"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11559_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "for applications for government financing, what percent of firms with canadian-born owners and of firms with immigrant owners were fully approved, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["84.2", "76.5"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11559_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_115_totto13338-2_0_1", "source": "hitab", "table_key": "01f914c9a8ec2495e9192017085f9a0d162203eb", "grid": {"TD": 2, "LD": 1, "nrows": 12, "ncols": 4, "section_rows": [], "cells": [["", "films", "films", "films"], ["", "anchorman: the legend of ron burgundy", "wake up, ron burgundy: the lost movie", "anchorman 2: the legend continues"], ["director", "adam mckay", "adam mckay", "adam mckay"], ["producer", "judd apatow", "judd apatow", "judd apatow will ferrell adam mckay"], ["writer", "will ferrell adam mckay", "will ferrell adam mckay", "will ferrell adam mckay"], ["composer", "alex wurman", "alex wurman", "andrew feltenstein john nau"], ["cinematographer", "thomas e. ackerman", "thomas e. ackerman", "oliver wood"], ["editor", "brent white", "brent white", "brent white melissa bretherton"], ["production companies", "apatow productions", "apatow productions", "apatow productions gary sanchez productions"], ["distributor", "dreamworks pictures", "dreamworks pictures", "paramount pictures"], ["running time", "94 minutes", "93 minutes", "119 minutes"], ["release date", "july 9, 2004", "december 28, 2004", "december 18, 2013"]]}, "native_split": "test", "question": "what were the anchorman series respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["anchorman: the legend of ron burgundy", "wake up, ron burgundy: the lost movie", "anchorman 2: the legend continues"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_115_totto13338-2_0_1", "source_table_id": "115_totto13338-2"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11558_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "what percent of lease applications were fully approved by firms with canadian-born owners aand by firms with immigrant owners, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["91.7", "96.6"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11558_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_48_totto5394-2_0_1", "source": "hitab", "table_key": "91f53258695065b3b3e3adac51d00d7f225e46da", "grid": {"TD": 2, "LD": 4, "nrows": 30, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "total", "total", "first division", "first division", "challenge cup", "challenge cup", "league cup", "league cup", "scottish cup", "scottish cup"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["none", "gk", "scotland", "scott fox", "6", "0", "5+1", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "gk", "scotland", "david hutton", "31", "0", "27+1", "0", "1+0", "0", "1+0", "0", "1+0", "0"], ["none", "gk", "france", "ludovic roy", "8", "0", "4+0", "0", "2+0", "0", "2+0", "0", "0+0", "0"], ["none", "df", "trinidad and tobago", "marvin andrews", "6", "0", "5+1", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "craig barr", "1", "0", "0+0", "0", "1+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "bob harris", "37", "5", "32+0", "4", "2+0", "0", "2+0", "1", "0+1", "0"], ["none", "df", "scotland", "david lilley", "37", "1", "30+0", "1", "3+0", "0", "3+0", "0", "1+0", "0"], ["none", "df", "scotland", "marc mcausland", "32", "0", "21+4", "0", "3+0", "0", "3+0", "0", "1+0", "0"], ["none", "df", "scotland", "jordan mcmillan", "16", "0", "16+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "df", "scotland", "gerry mclauchlan", "10", "0", "4+2", "0", "0+1", "0", "0+2", "0", "1+0", "0"], ["none", "df", "scotland", "jamie mcquilken", "10", "0", "1+5", "0", "0+1", "0", "1+1", "0", "1+0", "0"], ["none", "df", "scotland", "craig reid", "38", "0", "32+0", "0", "3+0", "0", "3+0", "0", "0+0", "0"], ["none", "mf", "scotland", "jamie adams", "7", "0", "7+0", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "paul burns", "42", "7", "35+0", "6", "3+0", "0", "3+0", "1", "1+0", "0"], ["none", "mf", "scotland", "joe hamill", "11", "0", "5+6", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "stephen mckenna", "33", "1", "27+1", "1", "2+0", "0", "2+0", "0", "1+0", "0"], ["none", "mf", "scotland", "anthony mcparland", "4", "0", "0+4", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "rocco quinn", "37", "5", "29+2", "4", "1+1", "0", "1+2", "1", "1+0", "0"], ["none", "mf", "scotland", "neil scally", "11", "0", "9+1", "0", "1+0", "0", "0+0", "0", "0+0", "0"], ["none", "mf", "scotland", "steve tosh", "11", "2", "3+2", "1", "2+1", "1", "3+0", "0", "0+0", "0"], ["none", "mf", "scotland", "barry wilson", "18", "4", "5+7", "1", "2+0", "1", "3+0", "2", "0+1", "0"], ["none", "fw", "scotland", "derek holmes", "43", "12", "35+1", "12", "3+0", "0", "3+0", "0", "1+0", "0"], ["none", "fw", "scotland", "stewart kean", "35", "8", "14+15", "7", "1+2", "1", "1+1", "0", "1+0", "0"], ["none", "fw", "scotland", "leon knight", "6", "0", "3+3", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "willie mclaren", "40", "6", "28+6", "6", "1+2", "0", "0+2", "0", "1+0", "0"], ["none", "fw", "england", "sean o'connor", "11", "0", "4+7", "0", "0+0", "0", "0+0", "0", "0+0", "0"], ["none", "fw", "scotland", "david weatherston", "37", "10", "12+18", "8", "2+1", "0", "2+1", "2", "0+1", "0"], ["none", "fw", "scotland", "dennis wyness", "7", "2", "3+4", "2", "0+0", "0", "0+0", "0", "0+0", "0"]]}, "native_split": "test", "question": "list any cups that queen of the south f.c play for?", "source_prompt": "{table}\n{question}", "answer_dp": ["challenge cup", "scottish cup", "league cup"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_48_totto5394-2_0_1", "source_table_id": "48_totto5394-2"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_435_2376_1", "source": "hitab", "table_key": "058513518a60c324daf0fe5eac0929ed9093aa14", "grid": {"TD": 2, "LD": 3, "nrows": 8, "ncols": 10, "section_rows": [3], "cells": [["", "", "", "total", "type of municipality", "type of municipality", "type of municipality", "demographic weight of the population with an immigrant background in the neighbourhood", "demographic weight of the population with an immigrant background in the neighbourhood", "demographic weight of the population with an immigrant background in the neighbourhood"], ["", "", "", "", "central", "peripheral 1st ring", "peripheral 2nd ring", "less than 50%", "50% to less than 70%", "70% or greater"], ["number", "total", "", "4192535", "1810780", "1662817", "718938", "1086098", "1301284", "1805153"], ["percentage", "census metropolitan area of residence", "", "", "", "", "", "", "", ""], ["percentage", "census metropolitan area of residence", "montreal", "100", "54", "33", "12", "52", "30", "18"], ["percentage", "census metropolitan area of residence", "toronto", "100", "45", "45", "11", "15", "31", "54"], ["percentage", "census metropolitan area of residence", "vancouver", "100", "27", "31", "41", "33", "33", "34"], ["percentage", "census metropolitan area of residence", "total", "100", "43", "40", "17", "26", "31", "43"]]}, "native_split": "test", "question": "list all categories that correspond to the percentage of people with an immigrant background in the neighborhood.", "source_prompt": "{table}\n{question}", "answer_dp": ["less than 50%", "50% to less than 70%", "70% or greater"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_435_2376_1", "source_table_id": "435"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_140_totto16955-4_0_1", "source": "hitab", "table_key": "7434ca51765fedeeb18621a6ac73bf933cc59ea7", "grid": {"TD": 2, "LD": 4, "nrows": 30, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "allsvenskan", "allsvenskan", "2014-15 svenska cupen", "2014-15 svenska cupen", "2015-16 svenska cupen", "2015-16 svenska cupen", "svenska supercupen", "svenska supercupen", "total", "total"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["1", "gk", "est", "andreas vaikla", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["4", "df", "swe", "andreas johansson", "29", "2", "4", "0", "1", "0", "1", "0", "35", "2"], ["5", "fw", "swe", "christoffer nyman", "29", "10", "0", "0", "1", "2", "1", "1", "31", "13"], ["6", "df", "swe", "linus wahlqvist", "29", "3", "3", "0", "1", "0", "1", "0", "34", "3"], ["7", "mf", "swe", "alexander fransson", "29", "5", "4", "0", "1", "0", "1", "1", "35", "6"], ["8", "mf", "swe", "rawez lawan", "1", "1", "0", "0", "0", "1", "0", "0", "1", "2"], ["9", "mf", "isl", "arnor ingvi traustason", "29", "7", "4", "2", "0", "0", "1", "0", "34", "9"], ["10", "fw", "swe", "emir kujovic", "29", "21", "3", "0", "0", "0", "1", "1", "33", "22"], ["11", "df", "swe", "christopher telo", "18", "0", "4", "0", "0", "0", "0", "0", "22", "0"], ["14", "mf", "swe", "nicklas barkroth", "15", "0", "4", "2", "1", "0", "1", "0", "21", "2"], ["15", "df", "swe", "marcus falk-olander", "2", "0", "1", "0", "0", "0", "0", "0", "3", "0"], ["16", "fw", "swe", "joel enarsson", "1", "0", "1", "0", "0", "0", "0", "0", "2", "0"], ["17", "fw", "sle", "alhaji kamara", "3", "6", "4", "3", "1", "2", "0", "0", "8", "11"], ["20", "mf", "fin", "daniel sjolund", "28", "2", "4", "0", "1", "0", "1", "0", "34", "2"], ["21", "df", "swe", "andreas hadenius", "6", "0", "0", "0", "0", "0", "0", "0", "6", "0"], ["23", "df", "swe", "david boo wiklander", "22", "0", "0", "0", "1", "0", "1", "0", "24", "0"], ["24", "mf", "swe", "gentrit citaku", "1", "0", "0", "1", "1", "0", "0", "0", "2", "1"], ["25", "df", "swe", "filip dagerstal", "4", "1", "3", "0", "0", "0", "0", "0", "7", "1"], ["26", "df", "swe", "adnan kojic", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["27", "mf", "swe", "tesfaldet tekie", "2", "0", "0", "0", "0", "0", "0", "0", "2", "0"], ["30", "df", "cro", "nikola tkalcic", "23", "2", "1", "0", "1", "0", "1", "0", "26", "2"], ["91", "gk", "mkd", "david mitov nilsson", "30", "0", "2", "0", "1", "0", "1", "0", "34", "0"], ["players out on loan", "", "", "", "", "", "", "", "", "", "", "", "none", "none"], ["19", "mf", "swe", "mirza halvadzic", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["29", "gk", "swe", "edvard setterberg", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["players that left the club during the season", "", "", "", "", "", "", "", "", "", "", "", "none", "none"], ["1", "gk", "swe", "marcus sahlman", "0", "0", "2", "0", "0", "0", "0", "0", "2", "0"], ["22", "df", "crc", "christopher meneses", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"]]}, "native_split": "test", "question": "what the matches did the ifk norrkoping club compete in?", "source_prompt": "{table}\n{question}", "answer_dp": ["allsvenskan", "2014-15 svenska cupen", "2015-16 svenska cupen"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_140_totto16955-4_0_1", "source_table_id": "140_totto16955-4"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2441_13033_1", "source": "hitab", "table_key": "6b79c47e112fe544cfede42558dfc0bd1312b6ec", "grid": {"TD": 3, "LD": 3, "nrows": 52, "ncols": 15, "section_rows": [4, 5, 9, 12, 13, 17, 20, 21, 25, 28, 29, 33, 36, 37, 41, 44, 45, 49], "cells": [["", "", "", "srr", "srr", "1991 to 2010 % change", "p", "annual % change", "annual % change", "annual % change", "forecast srr 2014", "2010 to 2014 % change", "annual % change", "annual % change", "annual % change"], ["", "", "", "1991", "2010", "", "", "", "95% confidence interval", "95% confidence interval", "", "", "", "95% confidence interval", "95% confidence interval"], ["", "", "", "", "", "", "", "", "from", "to", "", "", "", "from", "to"], ["aboriginal", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["total", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["total", "unintentional injury", "", "2.87", "1.15", "-59.9", "0.001", "-4.7", "-5.4", "-4", "0.79", "-31.5", "-9", "-22.7", "0.3"], ["total", "unintentional injury", "transport vehicle", "3.6", "0.57", "-84.1", "0", "-9.2", "-10.6", "-7.9", "-0.07", "", "", "", "9.6"], ["total", "unintentional injury", "medical/surgical mishap", "1.89", "1.24", "-34.1", "0.181", "-2.2", "-3.7", "-0.6", "1.09", "-12.3", "-3.2", "-9.5", "2"], ["total", "unintentional injury", "fall", "2.88", "1.47", "-49", "0.039", "-3.5", "-4.6", "-2.3", "1.17", "-20.2", "-5.5", "-20.2", "4.4"], ["total", "self-inflicted injury", "", "4.74", "1.81", "-61.9", "0.358", "-5", "-7.2", "-2.6", "1.5", "-16.8", "-4.5", "-15", "3.4"], ["total", "self-inflicted injury", "poisoning", "4.29", "1.78", "-58.5", "0.479", "-4.5", "-7", "-2", "1.59", "-10.8", "-2.8", "-12.8", "4.8"], ["total", "intentionally inflicted by another", "", "6.54", "3.23", "-50.7", "0.516", "-3.6", "-6.4", "-0.9", "3.55", "10.1", "2.4", "-7.5", "10.1"], ["male", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["male", "unintentional injury", "", "2.77", "1.14", "-58.7", "0.204", "-4.5", "-5.4", "-3.7", "1.16", "1.5", "0.4", "-7.2", "6.6"], ["male", "unintentional injury", "transport vehicle", "3.58", "0.6", "-83.2", "0.001", "-8.9", "-10.6", "-7.3", "0.66", "9.7", "2.3", "-50.2", "20.9"], ["male", "unintentional injury", "medical/surgical mishap", "1.49", "1.44", "-3.2", "0.011", "-0.2", "-2.4", "2.1", "1.39", "-3.7", "-1", "-11.5", "7"], ["male", "unintentional injury", "fall", "2.64", "1.62", "-38.9", "0.969", "-2.6", "-4.2", "-0.9", "1.4", "-13.4", "-3.5", "-16.7", "5.8"], ["male", "self-inflicted injury", "", "3.87", "1.83", "-52.7", "0.844", "-3.9", "-7.8", "0.3", "1.4", "-23.4", "-6.5", "-26.8", "5.6"], ["male", "self-inflicted injury", "poisoning", "2.61", "1.6", "-38.6", "0.528", "-2.5", "-7.1", "2.2", "1.63", "2.1", "0.5", "-24.3", "14.4"], ["male", "intentionally inflicted by another", "", "5.5", "2.64", "-51.9", "0.377", "-3.8", "-6.8", "-0.7", "2.04", "-22.7", "-6.2", "-48.7", "10.2"], ["female", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["female", "unintentional injury", "", "3.06", "1.17", "-61.8", "0", "-4.9", "-6", "-3.9", "0.77", "-34", "-9.9", "-27.1", "0.9"], ["female", "unintentional injury", "transport vehicle", "3.63", "0.51", "-86.1", "0.001", "-9.9", "-12.1", "-7.5", "0.14", "-72.6", "-27.6", "", "2.8"], ["female", "unintentional injury", "medical/surgical mishap", "2.27", "1.07", "-53", "0.597", "-3.9", "-6", "-1.8", "1.09", "1.7", "0.4", "-9.3", "7.9"], ["female", "unintentional injury", "fall", "3.21", "1.35", "-57.9", "0.005", "-4.5", "-6.1", "-2.8", "0.96", "-29", "-8.2", "-35", "5.6"], ["female", "self-inflicted injury", "", "5.15", "1.79", "-65.2", "0.224", "-5.4", "-8.2", "-2.5", "1.55", "-13.3", "-3.5", "-17.9", "6.4"], ["female", "self-inflicted injury", "poisoning", "4.96", "1.86", "-62.4", "0.293", "-5", "-7.9", "-2", "1.58", "-15.1", "-4", "-18.5", "5.9"], ["female", "intentionally inflicted by another", "", "10.86", "5.76", "-47", "0.814", "-3.3", "-9.4", "3.3", "5.27", "-8.5", "-2.2", "-18.6", "8.6"], ["total british columbia", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["total british columbia", "unintentional injury", "", "1.44", "0.73", "-49.3", "", "-3.5", "-3.6", "-3.4", "0.58", "-20.5", "-5.6", "-11.8", "-0.4"], ["total british columbia", "unintentional injury", "transport vehicle", "1.67", "0.52", "-68.7", "", "-5.9", "-6.2", "-5.6", "0.28", "-46.2", "-14.4", "-53.7", "0.7"], ["total british columbia", "unintentional injury", "medical/surgical mishap", "1.35", "0.72", "-46.4", "", "-3.2", "-3.5", "-3", "0.59", "-18.2", "-4.9", "-16.1", "3.3"], ["total british columbia", "unintentional injury", "fall", "1.28", "0.83", "-34.8", "", "-2.2", "-2.4", "-2", "0.74", "-11.3", "-2.9", "-6.8", "0.5"], ["total british columbia", "self-inflicted injury", "", "1.37", "0.65", "-52.4", "", "-3.8", "-4.3", "-3.3", "0.49", "-25.5", "-7.1", "-13.3", "-1.9"], ["total british columbia", "self-inflicted injury", "poisoning", "1.33", "0.66", "-50.2", "", "-3.6", "-4.1", "-3.1", "0.51", "-22.9", "-6.3", "-12.3", "-1.3"], ["total british columbia", "intentionally inflicted by another", "", "1.35", "0.8", "-40.7", "", "-2.7", "-3.3", "-2.1", "0.69", "-14.4", "-3.8", "-12.9", "3.2"], ["male", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["male", "unintentional injury", "", "1.5", "0.7", "-53.7", "", "-4", "-4.1", "-3.8", "0.53", "-24.4", "-6.7", "-14.8", "-0.4"], ["male", "unintentional injury", "transport vehicle", "1.63", "0.5", "-69.3", "", "-6", "-6.4", "-5.7", "0.26", "-47.5", "-14.9", "", "2.5"], ["male", "unintentional injury", "medical/surgical mishap", "1.37", "0.75", "-45.3", "", "-3.1", "-3.5", "-2.8", "0.62", "-17.5", "-4.7", "-16", "3.6"], ["male", "unintentional injury", "fall", "1.32", "0.81", "-38.5", "", "-2.5", "-2.8", "-2.2", "0.71", "-13.2", "-3.5", "-9", "1.3"], ["male", "self-inflicted injury", "", "1.37", "0.6", "-56.4", "", "-4.3", "-5.1", "-3.4", "0.45", "-24.6", "-6.8", "-14", "-0.9"], ["male", "self-inflicted injury", "poisoning", "1.28", "0.59", "-54.4", "", "-4", "-5", "-3.1", "0.5", "-14.6", "-3.9", "-11.6", "2.3"], ["male", "intentionally inflicted by another", "", "1.25", "0.79", "-36.5", "", "-2.4", "-3", "-1.7", "0.7", "-12.1", "-3.2", "-12.3", "4"], ["female", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["female", "unintentional injury", "", "1.37", "0.77", "-43.8", "", "-3", "-3.2", "-2.8", "0.64", "-16.4", "-4.4", "-9.8", "0.3"], ["female", "unintentional injury", "transport vehicle", "1.72", "0.56", "-67.7", "", "-5.8", "-6.3", "-5.3", "0.31", "-44", "-13.5", "-42.5", "0.2"], ["female", "unintentional injury", "medical/surgical mishap", "1.33", "0.7", "-47.4", "", "-3.3", "-3.7", "-3", "0.57", "-19", "-5.1", "-17.7", "3.8"], ["female", "unintentional injury", "fall", "1.25", "0.85", "-31.7", "", "-2", "-2.2", "-1.7", "0.77", "-9.8", "-2.5", "-6.3", "0.8"], ["female", "self-inflicted injury", "", "1.37", "0.68", "-50", "", "-3.6", "-4.2", "-2.9", "0.51", "-26", "-7.2", "-14.7", "-1.3"], ["female", "self-inflicted injury", "poisoning", "1.35", "0.7", "-48", "", "-3.4", "-4", "-2.7", "0.52", "-26.3", "-7.4", "-14.5", "-1.6"], ["female", "intentionally inflicted by another", "", "1.85", "0.84", "-54.5", "", "-4.1", "-5.6", "-2.5", "0.94", "11.8", "2.8", "-15.4", "14.6"]]}, "native_split": "test", "question": "what were the percent changes of the ssr of injury hospitalization for all unintentional causes among the aboriginal population and the total population respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["-59.9", "-49.3"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2441_13033_1", "source_table_id": "2441"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1625_9201_1", "source": "hitab", "table_key": "eff9e6498260092814996a0fb15f44168b6d54df", "grid": {"TD": 2, "LD": 3, "nrows": 10, "ncols": 11, "section_rows": [2, 6], "cells": [["", "", "", "total - 65 years and over", "total - 65 years and over", "65 to 74 years", "65 to 74 years", "75 to 84 years", "75 to 84 years", "85 years and over", "85 years and over"], ["", "", "", "women", "men", "women", "men", "women", "men", "women", "men"], ["percentage", "positive", "", "78.8", "78.6", "83", "81", "74.3", "76.2", "69", "67.9"], ["percentage", "positive", "excellent", "15.1", "14.3", "17.8", "15.7", "12.4", "12.8", "8.5", "8.9"], ["percentage", "positive", "very good", "30.5", "30.4", "32.1", "32.1", "29.4", "28.2", "25.1", "25.3"], ["percentage", "positive", "good", "33.1", "33.9", "33.1", "33.2", "32.5", "35.2", "35.4", "33.7"], ["percentage", "negative", "", "21.2", "21.4", "17", "19", "25.7", "23.8", "31", "32.1"], ["percentage", "negative", "fair", "15.5", "15.6", "12.8", "14", "18", "16.9", "22.8", "22.7"], ["percentage", "negative", "poor", "5.7", "5.9", "4.2", "4.9", "7.7", "6.9", "8.2", "9.4"], ["percentage", "total", "", "100", "100", "100", "100", "100", "100", "100", "100"]]}, "native_split": "test", "question": "which is more likely to rate their health in positive terms ,senior women aged 65 to 74 or senior men aged 65 to 74.", "source_prompt": "{table}\n{question}", "answer_dp": ["65 to 74 years", "women"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_1625_9201_1", "source_table_id": "1625"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_626_3520_1", "source": "hitab", "table_key": "523c16b3c01ff4ce22162f65a784028b2b1f5e1b", "grid": {"TD": 2, "LD": 2, "nrows": 18, "ncols": 7, "section_rows": [2, 9], "cells": [["", "", "number of postal codes erased and imputed", "percentage of postal codes erased and imputed", "rule a applied", "rule b applied", "perfect matches"], ["", "", "number", "percent", "percent", "percent", "percent"], ["experiment a", "", "", "", "", "", ""], ["experiment a", "1", "1572760", "90.6", "91.6", "8.4", "77"], ["experiment a", "2", "151305", "8.7", "91.5", "8.5", "72.1"], ["experiment a", "3", "10815", "0.6", "75.9", "24.1", "56.5"], ["experiment a", "4", "720", "0", "76.7", "23.3", "51.7"], ["experiment a", "5", "20", "0", "60", "40", "35"], ["experiment a", "total - experiment a", "1735620", "100", "91.5", "8.5", "76.4"], ["experiment b", "", "", "", "", "", ""], ["experiment b", "1", "2830755", "81.6", "91.4", "8.6", "76.8"], ["experiment b", "2", "547870", "15.8", "91.4", "8.6", "72"], ["experiment b", "3", "78510", "2.3", "76.1", "23.9", "56.8"], ["experiment b", "4", "10050", "0.3", "76.2", "23.8", "53.5"], ["experiment b", "5", "1090", "0", "58.2", "41.8", "38"], ["experiment b", "6", "115", "0", "59.6", "40.3", "29.8"], ["experiment b", "7", "15", "0", "64.3", "35.7", "35.7"], ["experiment b", "total - experiment b", "3468405", "100", "91", "9", "75.5"]]}, "native_split": "test", "question": "which gap years had a higher percentage of perfectly matched postal codes? one or two years gap or five or more years gap?", "source_prompt": "{table}\n{question}", "answer_dp": ["1", "2"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_626_3520_1", "source_table_id": "626"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_293_totto33611-2_0_1", "source": "hitab", "table_key": "b9a8099897d81e043dbd0d43d73b97cce7e6ae37", "grid": {"TD": 2, "LD": 4, "nrows": 33, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "total", "total", "third division", "third division", "scottish cup", "scottish cup", "league cup", "league cup", "challenge cup", "challenge cup"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["none", "gk", "scotland", "scott bain", "34", "0", "30", "0", "2", "0", "1", "0", "1", "0"], ["none", "gk", "scotland", "craig mcdowall", "8", "0", "8", "0", "0", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "mark docherty", "31", "4", "29", "4", "0", "0", "1", "0", "1", "0"], ["none", "df", "scotland", "michael doyle", "40", "2", "36", "2", "2", "0", "1", "0", "1", "0"], ["none", "df", "scotland", "fraser forrest", "7", "0", "5", "0", "2", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "ben gordon", "37", "5", "33", "5", "2", "0", "1", "0", "1", "0"], ["none", "df", "scotland", "ryan harding", "38", "1", "35", "1", "1", "0", "1", "0", "1", "0"], ["none", "df", "scotland", "mark mccullagh", "15", "0", "13", "0", "2", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "ross mckinnon", "16", "1", "16", "1", "0", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "ross philp", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "allan ramsey", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "stirling smith", "3", "0", "3", "0", "0", "0", "0", "0", "0", "0"], ["none", "df", "scotland", "kevin mchattie", "5", "0", "5", "0", "0", "0", "0", "0", "0", "0"], ["none", "mf", "scotland", "paul hartley", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["none", "mf", "scotland", "graeme holmes", "38", "3", "34", "3", "2", "0", "1", "0", "1", "0"], ["none", "mf", "scotland", "peter innes", "8", "0", "7", "0", "0", "0", "1", "0", "0", "0"], ["none", "mf", "scotland", "nicholas locke", "1", "0", "1", "0", "0", "0", "0", "0", "0", "0"], ["none", "mf", "scotland", "ross mccord", "31", "2", "28", "1", "1", "0", "1", "0", "1", "1"], ["none", "mf", "scotland", "ryan mccord", "36", "11", "32", "11", "2", "0", "1", "0", "1", "0"], ["none", "mf", "scotland", "darren young", "34", "2", "32", "2", "1", "0", "0", "0", "1", "0"], ["none", "mf", "scotland", "steven masterton", "16", "2", "14", "1", "2", "1", "0", "0", "0", "0"], ["none", "fw", "scotland", "ryan caddis", "4", "0", "2", "0", "0", "0", "1", "0", "1", "0"], ["none", "fw", "scotland", "craig campbell", "18", "3", "15", "3", "1", "0", "1", "0", "1", "0"], ["none", "fw", "scotland", "kevin cawley", "39", "10", "35", "9", "2", "1", "1", "0", "1", "0"], ["none", "fw", "scotland", "steven howarth", "2", "0", "2", "0", "0", "0", "0", "0", "0", "0"], ["none", "fw", "scotland", "kenny o'brien", "2", "0", "2", "0", "0", "0", "0", "0", "0", "0"], ["none", "fw", "france", "armand one", "18", "1", "14", "1", "2", "0", "1", "0", "1", "0"], ["none", "fw", "scotland", "max wright", "10", "1", "7", "0", "1", "0", "1", "0", "1", "1"], ["none", "fw", "scotland", "robbie winters", "28", "4", "27", "4", "1", "0", "0", "0", "0", "0"], ["none", "fw", "scotland", "stevie may", "22", "19", "22", "19", "0", "0", "0", "0", "0", "0"], ["none", "fw", "england", "matthew bradley", "18", "13", "9", "7", "4", "2", "4", "1", "1", "3"]]}, "native_split": "test", "question": "what cups did alloa compete in?", "source_prompt": "{table}\n{question}", "answer_dp": ["scottish cup", "league cup", "challenge cup"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_293_totto33611-2_0_1", "source_table_id": "293_totto33611-2"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_743_4164_1", "source": "hitab", "table_key": "9ff419feb42d51076ce4315d0f91960135360242", "grid": {"TD": 2, "LD": 1, "nrows": 7, "ncols": 4, "section_rows": [], "cells": [["", "total hate crime incidents", "total cyber-related hate crime incidents", "total cyber-related hate crime incidents"], ["", "number", "number", "percent"], ["2014", "1148", "47", "4.1"], ["2015", "1248", "61", "4.9"], ["2016", "1291", "59", "4.6"], ["2017", "1897", "87", "4.6"], ["2018", "1619", "82", "5.1"]]}, "native_split": "test", "question": "over the last five years, the proportion of police-reported hate crime incidents that were also recorded by police as cybercrimes has been fairly stable, what was the range of all hate crime incidents from 2014 to 2018?", "source_prompt": "{table}\n{question}", "answer_dp": ["4.1", "5.1"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_743_4164_1", "source_table_id": "743"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_9_totto1028-1_0_1", "source": "hitab", "table_key": "c20d16e9332267bda6e7e1ad50a0ba89d5b262d7", "grid": {"TD": 2, "LD": 4, "nrows": 37, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "total", "total", "ligue 1", "ligue 1", "coupe de france", "coupe de france", "coupe de la ligue", "coupe de la ligue", "champions league/europa league", "champions league/europa league"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["1", "gk", "croatia", "danijel subasic", "46", "0", "36", "0", "0", "0", "0", "0", "10", "0"], ["2", "df", "brazil", "fabinho", "47", "8", "34", "6", "3", "2", "1", "0", "9", "0"], ["4", "df", "portugal", "fabio coentrao", "19", "3", "13+2", "3", "0+1", "0", "0", "0", "3", "0"], ["5", "df", "brazil", "jemerson", "5", "0", "3+1", "0", "1", "0", "0", "0", "0", "0"], ["6", "df", "portugal", "ricardo carvalho", "44", "2", "33", "2", "2", "0", "1", "0", "8", "0"], ["7", "mf", "morocco", "nabil dirar", "28", "2", "20", "2", "0+1", "0", "0", "0", "4+3", "0"], ["8", "mf", "portugal", "joao moutinho", "37", "1", "24+2", "1", "3", "0", "0", "0", "8", "0"], ["9", "fw", "brazil", "vagner love", "13", "4", "9+3", "4", "0+1", "0", "0", "0", "0", "0"], ["10", "mf", "portugal", "bernardo silva", "44", "7", "22+10", "7", "3", "0", "1", "0", "8", "0"], ["11", "fw", "argentina", "guido carrillo", "40", "5", "15+16", "4", "1+1", "0", "0", "0", "0+7", "1"], ["12", "mf", "france", "fares bahlouli", "11", "2", "2+6", "1", "1", "1", "0", "0", "0+2", "0"], ["13", "df", "brazil", "wallace", "34", "1", "25+1", "1", "2", "0", "1", "0", "5", "0"], ["14", "mf", "france", "tiemoue bakayoko", "23", "2", "14+5", "1", "1+1", "1", "1", "0", "1", "0"], ["16", "gk", "france", "paul nardi", "6", "0", "2", "0", "3", "0", "1", "0", "0", "0"], ["17", "fw", "portugal", "ivan cavaleiro", "19", "3", "6+6", "1", "0", "0", "1", "0", "5+1", "2"], ["18", "mf", "portugal", "helder costa", "28", "5", "15+10", "3", "3", "2", "0", "0", "0", "0"], ["19", "fw", "ivory coast", "lacina traore", "27", "10", "11+8", "3", "2", "5", "0", "0", "5+1", "2"], ["20", "mf", "croatia", "mario pasalic", "29", "7", "12+4", "3", "1+2", "2", "1", "0", "8+1", "2"], ["21", "df", "nigeria", "elderson echiejile", "27", "1", "18+2", "0", "0", "0", "0+1", "0", "3+3", "1"], ["23", "mf", "mali", "adama traore", "8", "0", "4+2", "0", "0", "0", "0", "0", "1+1", "0"], ["24", "df", "italy", "andrea raggi", "44", "4", "29+2", "2", "3", "1", "1", "0", "9", "1"], ["27", "mf", "france", "thomas lemar", "34", "5", "21+5", "5", "1+1", "0", "0+1", "0", "2+3", "0"], ["28", "mf", "france", "jeremy toulalan", "36", "0", "25", "0", "1", "0", "0+1", "0", "9", "0"], ["29", "fw", "france", "kyllian mbappe lottin", "13", "1", "2+8", "1", "1", "0", "1", "0", "0+1", "0"], ["34", "df", "france", "raphael diarra", "1", "0", "0", "0", "0", "0", "0", "0", "0+1", "0"], ["38", "df", "mali", "almamy toure", "13", "3", "9+1", "3", "1", "0", "0", "0", "1+1", "0"], ["39", "fw", "france", "kylian mbappe", "6", "0", "0+3", "0", "0+1", "0", "1", "0", "0+1", "0"], ["none", "mf", "france", "mohamed chaibi", "2", "0", "0+1", "0", "0+1", "0", "0", "0", "0", "0"], ["players away from the club on loan:", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["29", "mf", "brazil", "gabriel boschilia", "6", "0", "1+4", "0", "0", "0", "1", "0", "0", "0"], ["25", "mf", "portugal", "rony lopes", "2", "0", "2", "0", "0", "0", "0", "0", "0", "0"], ["players who appeared for monaco no longer at the club:", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["3", "df", "france", "layvin kurzawa", "6", "3", "3", "1", "0", "0", "0", "0", "3", "2"], ["9", "fw", "france", "anthony martial", "7", "1", "2+1", "0", "0", "0", "0", "0", "4", "1"], ["22", "fw", "italy", "stephan el shaarawy", "24", "3", "7+8", "0", "0", "0", "0", "0", "4+5", "3"]]}, "native_split": "test", "question": "what league matches did monaco participate in?", "source_prompt": "{table}\n{question}", "answer_dp": ["ligue 1", "coupe de france", "coupe de la ligue", "champions league/europa league"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_9_totto1028-1_0_1", "source_table_id": "9_totto1028-1"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2480_13246_1", "source": "hitab", "table_key": "dfb10b3a2656dff7ad1f9b45f38467868f9d9b0e", "grid": {"TD": 2, "LD": 2, "nrows": 16, "ncols": 5, "section_rows": [2], "cells": [["", "", "ratio of listings to dwellings", "daily revenue per available listing", "daily revenue per available room"], ["", "", "%", "dollars", "dollars"], ["canada", "", "0.9", "37.2", "94.82"], ["canada", "newfoundland and labrador", "0.4", "25.77", "90.45"], ["canada", "prince edward island", "1.4", "49.06", "77.43"], ["canada", "nova scotia", "0.6", "40.97", "84.36"], ["canada", "new brunswick", "0.3", "26.26", "66.36"], ["canada", "quebec", "1.2", "31.16", "108.34"], ["canada", "ontario", "0.8", "36.61", "98.79"], ["canada", "manitoba", "0.2", "17.78", "76.63"], ["canada", "saskatchewan", "0.2", "14.36", "67.67"], ["canada", "alberta", "0.5", "23.83", "68.81"], ["canada", "british columbia", "1.7", "50.55", "111.45"], ["canada", "yukon", "1.4", "25.32", ""], ["canada", "northwest territories", "0.8", "41.06", ""], ["canada", "nunavut", "0.2", "12.34", ""]]}, "native_split": "test", "question": "which country had the second highest ratios of listings to dwellings?", "source_prompt": "{table}\n{question}", "answer_dp": ["prince edward island", "yukon"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_2480_13246_1", "source_table_id": "2480"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1979_11026_1", "source": "hitab", "table_key": "6501832b9c4be97a3c6f6d2cd31de1ded430b644", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 11, "section_rows": [], "cells": [["", "", "average sector output share", "average sector output share", "average sector output share", "average foreign-controlled market share", "average foreign-controlled market share", "average foreign-controlled market share", "percentage change in foreign control", "percentage change in foreign control", "percentage change in foreign control"], ["", "", "1973 to 1975", "1997 to 1999", "2009 to 2011", "1973 to 1975", "1997 to 1999", "2009 to 2011", "between the 1973-to-1975 and 1997-to-1999 periods", "between the 1997-to-1999 and 2009-to-2011 periods", "between the 1973-to-1975 and 2009-to-2011 periods"], ["percent", "food, beverage and tobacco", "19.6", "13.7", "17.7", "35.8", "46.7", "32.8", "30.5", "-29.9", "-8.5"], ["percent", "natural-resources-based", "15.5", "14.7", "26.3", "57.9", "40.8", "48.4", "-29.5", "18.7", "-16.3"], ["percent", "labour-intensive", "14.4", "10.1", "8.8", "34.6", "29.6", "21.4", "-14.4", "-27.7", "-38.1"], ["percent", "scale-based", "34.8", "41.8", "29.1", "57", "61.1", "58", "7.2", "-5.2", "1.6"], ["percent", "product-differentiated", "8.6", "9.7", "9", "56.2", "47.9", "34", "-14.7", "-29", "-39.5"], ["percent", "science-based", "7", "10", "9.1", "72.6", "50.8", "46.6", "-30", "-8.2", "-35.8"], ["percent", "manufacturing", "100", "100", "100", "50.8", "50.7", "44.6", "-0.2", "-12.1", "-12.3"]]}, "native_split": "test", "question": "what were the shares of manufacturing output of labour-intensive industries between 1973 to 1975 and 1997 to 1999 respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["14.4", "10.1"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1979_11026_1", "source_table_id": "1979"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11562_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "wha is the average amount of lease financing for immigrant-owned firms and for firms with canadian-born owners, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["79.9", "86.7"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11562_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_433_2366_1", "source": "hitab", "table_key": "d5f0798eab4a9f2521a593d9fcf943941c1c4f26", "grid": {"TD": 3, "LD": 2, "nrows": 17, "ncols": 6, "section_rows": [3, 8], "cells": [["", "", "total population", "type of population", "type of population", "type of population"], ["", "", "thousands", "immigrant", "second generation", "population with an immigrant background"], ["", "", "", "percentage", "percentage", "percentage"], ["census metropolitan area of vancouver", "", "2272.7", "40.1", "15.7", "55.8"], ["census metropolitan area of vancouver", "langley, dm", "103.1", "18", "10.7", "28.7"], ["census metropolitan area of vancouver", "surrey", "463.3", "40.5", "18.9", "59.5"], ["census metropolitan area of vancouver", "delta", "98.7", "28.9", "14.9", "43.8"], ["census metropolitan area of vancouver", "richmond", "189.3", "59.6", "15.4", "75"], ["vancouver", "", "590.2", "43.8", "17", "60.8"], ["vancouver", "burnaby", "220.3", "50.5", "17.8", "68.2"], ["vancouver", "new westminster", "65.1", "33.4", "14.6", "48"], ["vancouver", "coquitlam", "125", "41.7", "13.7", "55.3"], ["vancouver", "port coquitlam", "55.8", "29.4", "12.9", "42.3"], ["vancouver", "north vancouver, dm", "83.6", "29.9", "11.9", "41.7"], ["vancouver", "north vancouver, cy", "47.7", "37.2", "12", "49.2"], ["vancouver", "west vancouver", "42", "40.7", "11.4", "52.2"], ["vancouver", "maple ridge", "75.1", "17.2", "9.9", "27.1"]]}, "native_split": "test", "question": "list municipalities in vancouver, surrey, richmond, burnaby and coquitlam with at least 50 % of population with an immigrant background.", "source_prompt": "{table}\n{question}", "answer_dp": ["vancouver", "surrey", "richmond", "burnaby", "coquitlam"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_433_2366_1", "source_table_id": "433"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_491_2672_1", "source": "hitab", "table_key": "535289414e454905de53b18849b9ff49409e8c21", "grid": {"TD": 2, "LD": 2, "nrows": 21, "ncols": 5, "section_rows": [2, 5, 8, 11, 14, 17], "cells": [["", "", "%", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to"], ["discomfort or injury involving a laser product", "", "", "", ""], ["discomfort or injury involving a laser product", "yes", "1.1", "0.7", "1.4"], ["discomfort or injury involving a laser product", "no", "98.9", "98.5", "99.2"], ["discomfort or injury involving laser product-number of times", "", "", "", ""], ["discomfort or injury involving laser product-number of times", "1 time", "44.9", "27.6", "62.1"], ["discomfort or injury involving laser product-number of times", "2 to 260 times", "49.5", "32.2", "66.7"], ["type of discomfort or injury", "", "", "", ""], ["type of discomfort or injury", "skin", "41.3", "24.1", "58.5"], ["type of discomfort or injury", "eye", "59.1", "41.9", "76.3"], ["length of discomfort or injury", "", "", "", ""], ["length of discomfort or injury", "two days or less", "63.9", "45.2", "82.6"], ["length of discomfort or injury", "more than two days", "34", "15.2", "52.9"], ["own use or someone else's", "", "", "", ""], ["own use or someone else's", "your own use of the device", "25", "12.1", "37.8"], ["own use or someone else's", "someone else's use of the device", "74.9", "62", "87.7"], ["type of laser that caused injury, excluding medical devices", "", "", "", ""], ["type of laser that caused injury, excluding medical devices", "pointer", "26.3", "11.7", "40.9"], ["type of laser that caused injury, excluding medical devices", "cosmetic treatments", "39.1", "21.7", "56.6"], ["type of laser that caused injury, excluding medical devices", "survey tool / entertainment / materials processing / scanner / other", "34.1", "18.4", "49.8"]]}, "native_split": "test", "question": "what types of discomfort or injury can a laser product bring?", "source_prompt": "{table}\n{question}", "answer_dp": ["skin", "eye"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_491_2672_1", "source_table_id": "491"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_781_4463_1", "source": "hitab", "table_key": "6738f119cb16ce2677d53d850cca1728336bba9d", "grid": {"TD": 2, "LD": 2, "nrows": 29, "ncols": 11, "section_rows": [2, 8, 13, 18], "cells": [["", "", "crude rate per 100,000", "95% confidence interval", "95% confidence interval", "age-standardized rate per 100,000", "95% confidence interval", "95% confidence interval", "age-standardized rate ratio", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to", "", "from", "to", "", "from", "to"], ["household income quintile", "", "", "", "", "", "", "", "", "", ""], ["household income quintile", "quintile 1", "23.4", "21", "25.8", "25.8", "23.2", "28.5", "3.9", "3.4", "4.6"], ["household income quintile", "2", "12.7", "10.9", "14.5", "13.6", "11.5", "15.6", "2.1", "1.8", "2.4"], ["household income quintile", "3", "9", "7.6", "10.4", "9.3", "7.8", "10.7", "1.4", "1.2", "1.6"], ["household income quintile", "4", "7", "5.9", "8.1", "7.2", "6", "8.4", "1.1", "0.9", "1.3"], ["household income quintile", "quintile 5", "6.6", "5.5", "7.8", "6.6", "5.6", "7.6", "", "", ""], ["highest level of educational attainment", "", "", "", "", "", "", "", "", "", ""], ["highest level of educational attainment", "no certificate, diploma or degree", "23", "20.4", "25.7", "26.3", "23", "29.6", "2.1", "1.9", "2.4"], ["highest level of educational attainment", "high school diploma (or equivalent) and/or trade certificate", "14.3", "12.8", "15.8", "14.6", "13", "16.1", "1.2", "1", "1.3"], ["highest level of educational attainment", "college diploma or university below bachelor's level", "12.7", "11.1", "14.4", "12.4", "10.7", "14.1", "", "", ""], ["highest level of educational attainment", "university degree", "5.5", "4.3", "6.7", "5.8", "4.4", "7.2", "0.5", "0.4", "0.5"], ["labour force status", "", "", "", "", "", "", "", "", "", ""], ["labour force status", "employed", "6.5", "5.8", "7.2", "6.6", "5.9", "7.4", "", "", ""], ["labour force status", "unemployed", "20.1", "14.5", "25.6", "20.4", "13.9", "26.9", "3.1", "2.8", "3.5"], ["labour force status", "not in labour force", "30.8", "27.8", "33.7", "32.5", "29.3", "35.7", "4.8", "3", "7.5"], ["labour force status", "not in labour force", "19.6", "16.6", "22.5", "19.6", "16.6", "22.5", "3", "2.6", "3.3"], ["occupation", "", "", "", "", "", "", "", "", "", ""], ["occupation", "management", "5.1", "2.8", "7.4", "6.4", "3.6", "9.2", "0.7", "0.6", "0.9"], ["occupation", "business, finance and administration", "7.3", "5.6", "8.9", "7", "5.4", "8.6", "0.8", "0.6", "0.9"], ["occupation", "natural and applied sciences and related", "3", "1.9", "4", "2.8", "1.6", "4", "0.3", "0.2", "0.4"], ["occupation", "health", "9.2", "5.7", "12.8", "8.3", "5.1", "11.4", "0.9", "0.7", "1.1"], ["occupation", "education, law, social, community, and government services", "6.8", "5", "8.6", "6.6", "4.9", "8.3", "0.7", "0.6", "0.9"], ["occupation", "art, culture, recreation, sport", "6.1", "3.6", "8.6", "7", "3.5", "10.5", "0.8", "0.6", "0.9"], ["occupation", "sales and service", "9.5", "7.7", "11.3", "9.1", "7.2", "11", "", "", ""], ["occupation", "trades, transport, equipment operators, and related", "9.7", "7.9", "11.5", "9.5", "7.7", "11.3", "1", "0.9", "1.3"], ["occupation", "natural resources, agriculture, and related production", "6.7", "3.8", "9.7", "6.6", "3.5", "9.6", "0.7", "0.6", "0.9"], ["occupation", "manufacturing, and utilities", "8.2", "5.2", "11.2", "12.7", "2.4", "23.1", "1.4", "1.1", "1.7"]]}, "native_split": "test", "question": "what was the range of the crude rate of hops inversely related to household income?", "source_prompt": "{table}\n{question}", "answer_dp": ["6.6", "23.4"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_781_4463_1", "source_table_id": "781"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1468_8039_1", "source": "hitab", "table_key": "3d9b8504b08d619fd8e7033a11dfdd6a6b7d737b", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 8, "section_rows": [2, 5], "cells": [["", "", "single responses", "single responses", "multiple responses", "multiple responses", "total", "total"], ["", "", "number", "percentage", "number", "percentage", "number", "percentage"], ["official languages", "", "26627555", "97", "818635", "3", "27446190", "100"], ["official languages", "english", "19460850", "96.4", "732485", "3.6", "20193335", "100"], ["official languages", "french", "7166705", "96.2", "285375", "3.8", "7452075", "100"], ["non-official languages", "", "7321065", "91.8", "653310", "8.2", "7974375", "100"], ["non-official languages", "indigenous languages", "195700", "91.8", "17530", "8.2", "213230", "100"], ["non-official languages", "non-indigenous languages", "7125365", "91.8", "635785", "8.2", "7761150", "100"], ["total", "", "33948620", "97.6", "818635", "2.4", "34767250", "100"]]}, "native_split": "test", "question": "what were the percentages of multiple response rates for non-official languages and for french and for english as a mother tongue respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["8.2", "3.8", "3.6"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1468_8039_1", "source_table_id": "1468"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_271_totto30784-3_0_1", "source": "hitab", "table_key": "88ae2ac61aea85749518b8cd6af31d98d5306f32", "grid": {"TD": 2, "LD": 1, "nrows": 9, "ncols": 7, "section_rows": [], "cells": [["", "2003-2007", "2003-2007", "2008-2012", "2008-2012", "2013", "2013"], ["", "ordinary dividend tax rate", "qualified dividend tax rate", "ordinary dividend tax rate", "qualified dividend tax rate", "ordinary dividend tax rate", "qualified dividend tax rate"], ["10%", "10", "5", "10", "0", "10", "0"], ["15%", "15", "5", "15", "0", "15", "0"], ["25%", "25", "15", "25", "15", "25", "15-18.8*%"], ["28%", "28", "15", "28", "15", "28", "15-18.8*%"], ["33%", "33", "15", "33", "15", "33", "15-18.8*%"], ["35%", "35", "15", "35", "15", "35", "15-18.8*%"], ["39.6%", "", "", "", "", "39.6", "20-23.8*%"]]}, "native_split": "test", "question": "what is the range of the rates on qualified dividends?", "source_prompt": "{table}\n{question}", "answer_dp": ["0", "20-23.8*%"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_271_totto30784-3_0_1", "source_table_id": "271_totto30784-3"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2656_14356_1", "source": "hitab", "table_key": "fd98f0852093781eea8489b4372240730e8b9339", "grid": {"TD": 3, "LD": 2, "nrows": 16, "ncols": 6, "section_rows": [3], "cells": [["", "", "with a university degree", "with a university degree", "sample size", "sample size"], ["", "", "fathers", "mothers", "fathers", "mothers"], ["", "", "percent", "percent", "number", "number"], ["second generation groups", "", "", "", "", ""], ["second generation groups", "south asian", "30.8", "21.9", "11849", "11778"], ["second generation groups", "black", "16.8", "7.9", "7416", "10063"], ["second generation groups", "chinese", "33.6", "19.7", "9919", "10197"], ["second generation groups", "filipino", "33.2", "35.9", "2917", "3923"], ["second generation groups", "southeast asian", "18.5", "8.7", "2254", "2589"], ["second generation groups", "west asian/arab", "33.7", "21.7", "4662", "3839"], ["second generation groups", "latin american", "13.6", "9.6", "1896", "2644"], ["second generation groups", "korean", "42.5", "27.4", "830", "935"], ["second generation groups", "japanese", "38.1", "22.6", "351", "483"], ["second generation groups", "white", "17.8", "13.3", "74368", "71695"], ["second generation groups", "all second generation", "21.6", "15.2", "117167", "119011"], ["second generation groups", "third-plus generation whites", "15.2", "10.6", "492854", "560890"]]}, "native_split": "test", "question": "list the six groups that had university completion rates over twice as high as those of the fathers of third-plus generation whites.", "source_prompt": "{table}\n{question}", "answer_dp": ["southeast asian", "chinese", "filipino", "west asian/arab", "korean", "japanese"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_2656_14356_1", "source_table_id": "2656"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_491_2671_2", "source": "hitab", "table_key": "535289414e454905de53b18849b9ff49409e8c21", "grid": {"TD": 2, "LD": 2, "nrows": 21, "ncols": 5, "section_rows": [2, 5, 8, 11, 14, 17], "cells": [["", "", "%", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to"], ["discomfort or injury involving a laser product", "", "", "", ""], ["discomfort or injury involving a laser product", "yes", "1.1", "0.7", "1.4"], ["discomfort or injury involving a laser product", "no", "98.9", "98.5", "99.2"], ["discomfort or injury involving laser product-number of times", "", "", "", ""], ["discomfort or injury involving laser product-number of times", "1 time", "44.9", "27.6", "62.1"], ["discomfort or injury involving laser product-number of times", "2 to 260 times", "49.5", "32.2", "66.7"], ["type of discomfort or injury", "", "", "", ""], ["type of discomfort or injury", "skin", "41.3", "24.1", "58.5"], ["type of discomfort or injury", "eye", "59.1", "41.9", "76.3"], ["length of discomfort or injury", "", "", "", ""], ["length of discomfort or injury", "two days or less", "63.9", "45.2", "82.6"], ["length of discomfort or injury", "more than two days", "34", "15.2", "52.9"], ["own use or someone else's", "", "", "", ""], ["own use or someone else's", "your own use of the device", "25", "12.1", "37.8"], ["own use or someone else's", "someone else's use of the device", "74.9", "62", "87.7"], ["type of laser that caused injury, excluding medical devices", "", "", "", ""], ["type of laser that caused injury, excluding medical devices", "pointer", "26.3", "11.7", "40.9"], ["type of laser that caused injury, excluding medical devices", "cosmetic treatments", "39.1", "21.7", "56.6"], ["type of laser that caused injury, excluding medical devices", "survey tool / entertainment / materials processing / scanner / other", "34.1", "18.4", "49.8"]]}, "native_split": "test", "question": "what is the 95% confidence interval of those reported experiencing discomfort or injury within the past 12 months?", "source_prompt": "{table}\n{question}", "answer_dp": ["0.7", "1.4"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_491_2671_2", "source_table_id": "491"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2078_11564_1", "source": "hitab", "table_key": "b25c3612c9d137d4bb02d97368f655aec12e7214", "grid": {"TD": 3, "LD": 1, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["", "immigrant owner", "immigrant owner", "canadian-born owner", "canadian-born owner"], ["", "average value", "authorization rate", "average value", "authorization rate"], ["", "thousands of dollars", "percent", "thousands of dollars", "percent"], ["debt financing", "174.7", "76.9", "182.5", "81.7"], ["lease financing", "79.9", "91.7", "86.7", "96.6"], ["trade credit", "87.9", "97.2", "93.2", "98.6"], ["equity financing", "399.4", "79", "444.1", "65.6"], ["government financing", "162.7", "76.5", "80.5", "84.2"]]}, "native_split": "test", "question": "what is the government financing for immigrant-owned firms and for firms with canadian-born owners, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["162.7", "80.5"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2078_11564_1", "source_table_id": "2078"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1979_11022_1", "source": "hitab", "table_key": "6501832b9c4be97a3c6f6d2cd31de1ded430b644", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 11, "section_rows": [], "cells": [["", "", "average sector output share", "average sector output share", "average sector output share", "average foreign-controlled market share", "average foreign-controlled market share", "average foreign-controlled market share", "percentage change in foreign control", "percentage change in foreign control", "percentage change in foreign control"], ["", "", "1973 to 1975", "1997 to 1999", "2009 to 2011", "1973 to 1975", "1997 to 1999", "2009 to 2011", "between the 1973-to-1975 and 1997-to-1999 periods", "between the 1997-to-1999 and 2009-to-2011 periods", "between the 1973-to-1975 and 2009-to-2011 periods"], ["percent", "food, beverage and tobacco", "19.6", "13.7", "17.7", "35.8", "46.7", "32.8", "30.5", "-29.9", "-8.5"], ["percent", "natural-resources-based", "15.5", "14.7", "26.3", "57.9", "40.8", "48.4", "-29.5", "18.7", "-16.3"], ["percent", "labour-intensive", "14.4", "10.1", "8.8", "34.6", "29.6", "21.4", "-14.4", "-27.7", "-38.1"], ["percent", "scale-based", "34.8", "41.8", "29.1", "57", "61.1", "58", "7.2", "-5.2", "1.6"], ["percent", "product-differentiated", "8.6", "9.7", "9", "56.2", "47.9", "34", "-14.7", "-29", "-39.5"], ["percent", "science-based", "7", "10", "9.1", "72.6", "50.8", "46.6", "-30", "-8.2", "-35.8"], ["percent", "manufacturing", "100", "100", "100", "50.8", "50.7", "44.6", "-0.2", "-12.1", "-12.3"]]}, "native_split": "test", "question": "what was the percentage change in foreign controling at the science-based sector and product-differentiated sector during the period before 2000 respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["-30", "-14.7"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1979_11022_1", "source_table_id": "1979"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_81_321_1", "source": "hitab", "table_key": "17a4f3f727d0363ffda11b936af65e9b441cb7c3", "grid": {"TD": 2, "LD": 3, "nrows": 10, "ncols": 17, "section_rows": [2, 5], "cells": [["", "", "", "gaspesie iles-de-la-madeleine", "gaspesie iles-de-la-madeleine", "estrie", "estrie", "montegerie", "montegerie", "montreal - laval", "montreal - laval", "laurentides", "laurentides", "outaouais", "outaouais", "abitibi-temiscamingue nord-du-quebec", "abitibi-temiscamingue nord-du-quebec"], ["", "", "", "english-language workers", "other workers", "english-language workers", "other workers", "english-language workers", "other workers", "english-language workers", "other workers", "english-language workers", "other workers", "english-language workers", "other workers", "english-language workers", "other workers"], ["percent", "sex", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["percent", "sex", "female", "64", "56.8", "53.8", "54.6", "45.3", "49.3", "38.8", "44.5", "47.1", "54.9", "44.8", "48.8", "60.5", "58.4"], ["percent", "sex", "male", "36", "43.2", "46.2", "45.1", "54.7", "50.7", "61.2", "55.5", "52.9", "45.1", "55.3", "50.9", "40.1", "41.6"], ["percent", "marital status", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["percent", "marital status", "single", "40.4", "40.6", "48.4", "50.4", "41.3", "48.1", "46.1", "51.4", "41.7", "51.1", "54.8", "58.3", "56.5", "50.1"], ["percent", "marital status", "married", "35.1", "22.9", "29.1", "19.1", "41.8", "21.7", "40.6", "24.8", "36.9", "18.7", "28.2", "17.4", "22.6", "17"], ["percent", "marital status", "common-law", "17.5", "29.8", "14.8", "24.4", "10.7", "23.5", "6.5", "15.9", "14.5", "23.2", "10.3", "18.6", "18.1", "26.7"], ["percent", "marital status", "separated, divorced, or widowed", "7", "6.8", "7.1", "5.9", "6", "6.7", "6.8", "7.8", "7.1", "6.9", "6.8", "5.8", "2.8", "6.3"]]}, "native_split": "test", "question": "in the gaspesie-iles-de-la-madeleine region, how many percent of female workers are english-language agri-food workers and other workers.respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["64", "56.8"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_81_321_1", "source_table_id": "81"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2415_12935_1", "source": "hitab", "table_key": "c7988b9063edcd9f8a552612f9c5b231754f009a", "grid": {"TD": 2, "LD": 3, "nrows": 50, "ncols": 6, "section_rows": [2, 5, 14, 26, 33], "cells": [["", "", "", "non-immigrants", "immigrants", "immigrants"], ["", "", "", "", "studied in canada", "studied outside canada"], ["predicted probability", "sex", "", "", "", ""], ["predicted probability", "sex", "women", "3.6", "4.7", "15"], ["predicted probability", "sex", "men", "3.5", "4.4", "13.2"], ["predicted probability", "highest level of educational attainment", "", "", "", ""], ["predicted probability", "highest level of educational attainment", "bachelor's degree without further studies", "4.7", "6.4", "16.8"], ["predicted probability", "highest level of educational attainment", "bachelor's degree with further studies", "1.4", "2.7", "11.7"], ["predicted probability", "highest level of educational attainment", "university certificate or diploma above bachelor level without further studies", "1.8", "1.2", "13.8"], ["predicted probability", "highest level of educational attainment", "university certificate or diploma above bachelor level with further studies", "1", "0.7", "10.7"], ["predicted probability", "highest level of educational attainment", "degree in medicine, dentistry, veterinary medicine or optometry", "0.2", "0.5", "10.1"], ["predicted probability", "highest level of educational attainment", "master's degree without further studies", "1", "1.1", "9.5"], ["predicted probability", "highest level of educational attainment", "master's degree with further studies", "0.1", "ne", "2.1"], ["predicted probability", "highest level of educational attainment", "doctorate acquired", "0.2", "ne", "2.4"], ["predicted probability", "field of study", "", "", "", ""], ["predicted probability", "field of study", "education", "1.5", "3", "17.7"], ["predicted probability", "field of study", "visual and performing arts, and communications technologies", "6", "8.7", "15.1"], ["predicted probability", "field of study", "humanities", "8.9", "8.7", "24.8"], ["predicted probability", "field of study", "social and behavioural sciences and law", "5.6", "7.8", "19.7"], ["predicted probability", "field of study", "business, management and public administration", "3.3", "4.6", "15.9"], ["predicted probability", "field of study", "physical and life sciences and technologies", "4.2", "4.4", "14.5"], ["predicted probability", "field of study", "mathematics, computer and information sciences", "1.4", "1.5", "8"], ["predicted probability", "field of study", "architecture, engineering, and related technologies", "1", "2.9", "10.5"], ["predicted probability", "field of study", "agriculture, natural resources and conservation", "4.2", "7.2", "25.4"], ["predicted probability", "field of study", "health and related fields", "1.7", "2.6", "9.9"], ["predicted probability", "field of study", "other", "10.1", "ne", "18.3"], ["predicted probability", "level of familiarity with official languages", "", "", "", ""], ["predicted probability", "level of familiarity with official languages", "1. highest level", "3.3", "2.9", "7.9"], ["predicted probability", "level of familiarity with official languages", "2", "3.7", "3.8", "6"], ["predicted probability", "level of familiarity with official languages", "3", "3.7", "3.3", "10.4"], ["predicted probability", "level of familiarity with official languages", "4", "3.4", "4.8", "12.5"], ["predicted probability", "level of familiarity with official languages", "5", "4.5", "5.5", "14"], ["predicted probability", "level of familiarity with official languages", "6. lowest level", "5", "6.7", "16.1"], ["predicted probability", "location of study", "", "", "", ""], ["predicted probability", "location of study", "canada", "3.5", "", ""], ["predicted probability", "location of study", "north america", "", "", "7.3"], ["predicted probability", "location of study", "central and south america", "", "", "8.2"], ["predicted probability", "location of study", "caribbean and bermuda", "", "", "15.2"], ["predicted probability", "location of study", "western europe", "", "", "6.9"], ["predicted probability", "location of study", "eastern europe", "", "", "11.6"], ["predicted probability", "location of study", "northern europe", "", "", "5.2"], ["predicted probability", "location of study", "southern europe", "", "", "8.8"], ["predicted probability", "location of study", "sub-saharan africa", "", "", "10.2"], ["predicted probability", "location of study", "northern africa", "", "", "8.5"], ["predicted probability", "location of study", "west central asia", "", "", "10.4"], ["predicted probability", "location of study", "eastern asia", "", "", "10"], ["predicted probability", "location of study", "southeast asia", "", "", "23.6"], ["predicted probability", "location of study", "southern asia", "", "", "22"], ["predicted probability", "location of study", "oceania", "", "", "11.3"], ["predicted probability", "location of study", "outside canada", "4.7", "", ""]]}, "native_split": "test", "question": "what were the predicted probabilities of persistent overqualification for immigrants who completed their studies outside canada and whose highest level of educational attainment was a degree in medicine, dentistry, veterinary medicine or optometry and those non-immigrants respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["10.1", "0.2"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2415_12935_1", "source_table_id": "2415"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2610_14074_1", "source": "hitab", "table_key": "67f8a6f9c8d772ffe6809443c2edf182591eda63", "grid": {"TD": 2, "LD": 3, "nrows": 24, "ncols": 6, "section_rows": [6, 20], "cells": [["", "", "", "tercile", "tercile", "tercile"], ["", "", "", "lower", "middle", "upper"], ["number", "economic regions", "", "23", "23", "23"], ["thousands", "average number of employees aged 18 to 64", "", "360", "171", "59"], ["percent", "layoff rate", "", "4.7", "6.8", "12.6"], ["percent", "hiring rate", "", "19.9", "21.1", "23.8"], ["percent", "percentage of employees", "", "", "", ""], ["percent", "percentage of employees", "with 24 months of tenure or less", "32.9", "32.1", "33.9"], ["percent", "percentage of employees", "aged 18 to 34 years", "38.7", "37.5", "33.9"], ["percent", "percentage of employees", "aged 50 to 64 years", "24.2", "25", "27.2"], ["percent", "percentage of employees", "in agriculture, fishing, forestry and hunting", "1.7", "1.8", "3.4"], ["percent", "percentage of employees", "in accommodation and food services", "5.9", "6.1", "6.6"], ["percent", "percentage of employees", "in construction", "5.5", "6.2", "6.1"], ["percent", "percentage of employees", "in manufacturing", "12.1", "12.4", "11.7"], ["percent", "percentage of employees", "in small firms", "19.7", "20.4", "24.5"], ["percent", "percentage of employees", "in large firms", "49", "48.6", "44.2"], ["percent", "percentage of employees", "in full-time jobs", "84.4", "84.6", "84.7"], ["percent", "percentage of employees", "in temporary jobs", "11.1", "12.3", "18"], ["percent", "percentage of employees", "in term or contract jobs", "5.7", "6", "7"], ["percent", "percentage of employees", "in seasonal jobs", "2.6", "3.3", "6.8"], ["2014 dollars", "earnings and employment insurance benefits of employees", "", "", "", ""], ["2014 dollars", "earnings and employment insurance benefits of employees", "average annual wages per job held by employees", "31773", "29931", "26023"], ["2014 dollars", "earnings and employment insurance benefits of employees", "average annual wages per employee", "45260", "43064", "37602"], ["2014 dollars", "earnings and employment insurance benefits of employees", "average employment insurance income received by employees", "790", "1117", "2167"]]}, "native_split": "test", "question": "what are the average annual layoff rates fo ers in lower, middle, upper groups, respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["12.6", "6.8", "4.7"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2610_14074_1", "source_table_id": "2610"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2701_14613_1", "source": "hitab", "table_key": "60fb8e84a913d349acf55b312b3476172bf08aa4", "grid": {"TD": 1, "LD": 3, "nrows": 54, "ncols": 7, "section_rows": [3, 6, 12, 15, 20, 24, 27, 33, 38, 41, 47, 51], "cells": [["", "", "", "neither cyberbullied nor cyberstalked", "cyberbullied, but not cyberstalked", "cyberstalked, but not cyberbullied", "cyberbullied and cyberstalked"], ["percent", "all 15-to-29 year-olds", "", "83.2", "6", "5.6", "5.2"], ["percent", "sociodemographic", "", "", "", "", ""], ["percent", "sexual orientation", "", "", "", "", ""], ["percent", "sexual orientation", "heterosexual", "84.6", "5", "5.6", "4.8"], ["percent", "sexual orientation", "homosexual/bi-sexual", "66", "12.2", "9.6", "12.3"], ["percent", "age group", "", "", "", "", ""], ["percent", "age group", "15 to 17", "81", "9.3", "5", "4.8"], ["percent", "age group", "18 to 20", "80.5", "7.8", "5.6", "6.1"], ["percent", "age group", "21 to 23", "84", "5.1", "6.1", "4.8"], ["percent", "age group", "24 to 26", "84.5", "5", "5.7", "4.8"], ["percent", "age group", "27 to 29", "85.4", "3.5", "5.6", "5.5"], ["percent", "gender", "", "", "", "", ""], ["percent", "gender", "men", "85.2", "6.1", "4.7", "4.1"], ["percent", "gender", "women", "81.1", "6", "6.6", "6.4"], ["percent", "province/region of residence", "", "", "", "", ""], ["percent", "province/region of residence", "atlantic", "82.1", "6.8", "5.2", "5.9"], ["percent", "province/region of residence", "quebec", "86.6", "5.9", "4.1", "3.4"], ["percent", "province/region of residence", "ontario", "81", "7", "6", "6.1"], ["percent", "province/region of residence", "west", "83.7", "4.9", "6.2", "5.2"], ["percent", "population size of community", "", "", "", "", ""], ["percent", "population size of community", "rural", "84", "6.6", "3.7", "5.8"], ["percent", "population size of community", "population centre", "82.9", "5.9", "6.1", "5.1"], ["percent", "family, family background", "", "", "", "", ""], ["percent", "marital status", "", "", "", "", ""], ["percent", "marital status", "currently married or ever-married/common-law", "88.8", "4", "4.1", "3.2"], ["percent", "marital status", "single/never-married", "81.7", "6.6", "6", "5.7"], ["percent", "parental education", "", "", "", "", ""], ["percent", "parental education", "both have high school or less", "81.1", "6.7", "7.1", "5.1"], ["percent", "parental education", "at least one has postsecondary education", "84.6", "5.3", "5", "5"], ["percent", "parental education", "both have postsecondary education", "82.5", "6.2", "5.8", "5.5"], ["percent", "parental education", "do not know either parent's education level", "84.4", "6", "4.5", "5.1"], ["percent", "education/main activity/household income", "", "", "", "", ""], ["percent", "highest level of education", "", "", "", "", ""], ["percent", "highest level of education", "less than high school", "81.2", "9.1", "4.5", "5.3"], ["percent", "highest level of education", "high school", "81.3", "5.7", "7", "6"], ["percent", "highest level of education", "postsecondary - non-university", "86.7", "4.4", "5", "4"], ["percent", "highest level of education", "postsecondary - university", "84.3", "5.2", "5.2", "5.3"], ["percent", "main activity in the last year", "", "", "", "", ""], ["percent", "main activity in the last year", "non-student", "84.5", "4.9", "5.6", "4.9"], ["percent", "main activity in the last year", "student", "81.7", "7.2", "5.6", "5.5"], ["percent", "household income", "", "", "", "", ""], ["percent", "household income", "less than $40,000", "75.6", "6.3", "11.1", "7.1"], ["percent", "household income", "$40,000 to $79,999", "83.4", "7.1", "5.2", "4.3"], ["percent", "household income", "$80,000 or more", "85.3", "5", "5.1", "4.6"], ["percent", "household income", "not stated", "82.6", "6.3", "5.4", "5.6"], ["percent", "cultural/ethnic", "", "", "", "", ""], ["percent", "religious affiliation", "", "", "", "", ""], ["percent", "religious affiliation", "none", "80.6", "6.2", "6", "7.2"], ["percent", "religious affiliation", "christian", "84.4", "6.3", "5.2", "4.2"], ["percent", "religious affiliation", "non-christian", "83.8", "4.2", "7.4", "4.6"], ["percent", "visible minority", "", "", "", "", ""], ["percent", "visible minority", "no", "82.2", "6.5", "5.6", "5.8"], ["percent", "visible minority", "yes", "86.1", "4.8", "5.4", "3.7"]]}, "native_split": "test", "question": "what were the percentages of women and men who had been cyberstalked?", "source_prompt": "{table}\n{question}", "answer_dp": ["6.6", "4.7"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2701_14613_1", "source_table_id": "2701"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1048_6028_1", "source": "hitab", "table_key": "f2a30b23d1570242a54bc8468867295eaf1b9c2b", "grid": {"TD": 2, "LD": 3, "nrows": 25, "ncols": 8, "section_rows": [2], "cells": [["", "", "", "immigrant class", "immigrant class", "immigrant class", "immigrant class", "immigrant class"], ["", "", "", "family", "economic", "business", "dependants", "refugees"], ["percent distribution", "industry", "", "", "", "", "", ""], ["percent distribution", "industry", "construction of buildings", "3.5", "2.6", "1.8", "2", "4.9"], ["percent distribution", "industry", "specialty trade contractors", "6.9", "5", "2.6", "3.7", "10.1"], ["percent distribution", "industry", "manufacturing", "1.7", "1.5", "1.5", "1.3", "1.8"], ["percent distribution", "industry", "wholesale trade", "1", "1.3", "1.4", "1.1", "1"], ["percent distribution", "industry", "retail trade", "5.3", "6.6", "7.6", "6.7", "5.5"], ["percent distribution", "industry", "truck transportation", "5.4", "2.5", "1", "1.5", "6"], ["percent distribution", "industry", "transit and ground passenger transportation", "5.9", "5.1", "0.9", "1.1", "9.4"], ["percent distribution", "industry", "lessors of real estate", "13.9", "16.8", "37.9", "22.6", "11.5"], ["percent distribution", "industry", "offices of real estate agents and brokers", "1.6", "2.8", "1.5", "2.3", "1.4"], ["percent distribution", "industry", "accounting, tax preparation, bookkeeping and payroll services", "1.9", "2.8", "1.2", "2.5", "1.3"], ["percent distribution", "industry", "architectural, engineering and related services", "0.4", "1.6", "0.5", "0.7", "0.5"], ["percent distribution", "industry", "computer systems design and related services", "0.7", "2.5", "0.3", "1.4", "0.6"], ["percent distribution", "industry", "management, scientific and technical consulting services", "3.3", "7.4", "5.7", "4.9", "2.5"], ["percent distribution", "industry", "services to buildings and dwellings", "13", "10.5", "8.2", "11.9", "14.8"], ["percent distribution", "industry", "offices of physicians", "0.9", "2.1", "0.3", "1.8", "0.7"], ["percent distribution", "industry", "offices of dentists", "0.4", "0.4", "0.2", "0.9", "0.4"], ["percent distribution", "industry", "child day-care services", "10.7", "2.9", "1.3", "6.8", "5.3"], ["percent distribution", "industry", "food services and drinking places", "1", "0.9", "1", "0.9", "1.1"], ["percent distribution", "industry", "all other industries", "22.4", "24.6", "25", "26.1", "21"], ["percent distribution", "industry", "total", "100", "100", "100", "100", "100"], ["number", "total", "", "119094", "81116", "16070", "88902", "66056"], ["percent", "knowledge-based industries", "", "5.8", "13.8", "7.5", "9", "4.7"]]}, "native_split": "test", "question": "what are the top three prominent industries among family class immigrants in primary self-employment?", "source_prompt": "{table}\n{question}", "answer_dp": ["lessors of real estate", "services to buildings and dwellings", "child day-care services"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_1048_6028_1", "source_table_id": "1048"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2335_12533_1", "source": "hitab", "table_key": "2c20a233b56065c72081b775dea013494a1c7d99", "grid": {"TD": 2, "LD": 2, "nrows": 15, "ncols": 8, "section_rows": [], "cells": [["", "", "2015", "2015", "2016", "2016", "2017", "2017"], ["", "", "previously published", "revised growth", "previously published", "revised growth", "previously published", "revised growth"], ["percentage", "newfoundland and labrador", "-1.5", "-1.08", "1.72", "1.72", "2.1", "0.88"], ["percentage", "prince edward island", "1.51", "1.4", "2.19", "1.68", "3.17", "3.45"], ["percentage", "nova scotia", "1.16", "0.76", "1.05", "1.52", "1.22", "1.49"], ["percentage", "new brunswick", "2.3", "0.79", "1.2", "1.39", "1.95", "1.99"], ["percentage", "quebec", "1.01", "0.96", "1.53", "1.46", "3.08", "2.9"], ["percentage", "ontario", "2.78", "2.65", "2.62", "2.45", "2.79", "2.77"], ["percentage", "manitoba", "1.36", "1.37", "2.12", "1.67", "2.91", "3.13"], ["percentage", "saskatchewan", "-1.24", "-0.76", "-0.45", "-0.26", "2.86", "2.29"], ["percentage", "alberta", "-3.93", "-3.49", "-3.6", "-4.06", "4.85", "4.59"], ["percentage", "british columbia", "3.25", "2.32", "3.59", "3.36", "3.94", "4.01"], ["percentage", "yukon", "-7.94", "-7.6", "8.29", "7", "-1.44", "2.9"], ["percentage", "northwest territories", "1.24", "1.02", "0.83", "1.27", "5.25", "3.88"], ["percentage", "nunavut", "-0.66", "-0.41", "1.9", "3.46", "13.33", "10.31"]]}, "native_split": "test", "question": "among all the provinces, which provinces has the top two largest revision between the previously-published rate and the revised growth rate in 2016?", "source_prompt": "{table}\n{question}", "answer_dp": ["prince edward island", "nova scotia"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_2335_12533_1", "source_table_id": "2335"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1820_10273_1", "source": "hitab", "table_key": "00db50cf39fb75658ceb7d2c4148ee1d490165d8", "grid": {"TD": 3, "LD": 1, "nrows": 11, "ncols": 7, "section_rows": [], "cells": [["", "participation rate", "participation rate", "participation rate", "average time spent", "average time spent", "average time spent"], ["", "excellent or very good", "good", "fair or poor", "excellent or very good", "good", "fair or poor"], ["", "percent", "percent", "percent", "hours", "hours", "hours"], ["doing unpaid household work", "89", "87", "84", "3.4", "3.2", "2.9"], ["engaging in civic, religious and organizational activities", "10", "9", "5", "2.3", "1.9", "2.1"], ["socializing and communicating", "45", "41", "37", "2.3", "2.2", "2.4"], ["engaging in leisure and physical activities", "45", "39", "36", "2.2", "2.1", "2.3"], ["using technology", "32", "27", "25", "1.9", "2", "2.3"], ["watching television and videos", "82", "88", "85", "3.7", "4.2", "4.9"], ["reading", "42", "37", "32", "2", "2", "2"], ["sleeping", "100", "100", "100", "8.9", "9.1", "9.5"]]}, "native_split": "test", "question": "what were the percentages of seniors who reported their health as excellent or very good engaged in unpaid household work and seniors who reported their health as fair or poor respectively?", "source_prompt": "{table}\n{question}", "answer_dp": ["89", "84"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1820_10273_1", "source_table_id": "1820"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1837_10375_1", "source": "hitab", "table_key": "ea41c5d8eff6370e8c97c07a9f378e2c4f55eebc", "grid": {"TD": 2, "LD": 3, "nrows": 14, "ncols": 7, "section_rows": [2], "cells": [["", "", "", "men", "men", "women", "women"], ["", "", "", "canada", "united states", "canada", "united states"], ["percent", "industry", "", "", "", "", ""], ["percent", "industry", "agriculture, forestry, fishing and hunting", "2.5", "13.7", "17.3", "3"], ["percent", "industry", "mining, quarrying and oil and gas extraction", "26.4", "4.4", "25.9", "-1.1"], ["percent", "industry", "construction", "15.6", "-2.2", "20.3", "5.3"], ["percent", "industry", "manufacturing", "-2.2", "-4", "10.2", "0.6"], ["percent", "industry", "wholesale trade and retail trade", "2.9", "-6.4", "18.7", "1.4"], ["percent", "industry", "finance, insurance and real estate", "4.2", "-3.6", "16.2", "1.9"], ["percent", "industry", "educational services, health care and social assistance", "9.3", "-1.6", "10", "2.2"], ["percent", "industry", "accommodation and food services", "10.6", "2.1", "20.6", "7.5"], ["percent", "industry", "public administration", "18.4", "-5.1", "17.7", "-7.1"], ["percent", "industry", "other services", "2.8", "-8.9", "7.2", "-4"], ["percent", "all industries", "", "4.2", "-5.5", "9.3", "1.1"]]}, "native_split": "test", "question": "which sectors witnessed the canada-u.s. difference in wage growth amounted to 15 percentage points or more from 2000 to 2017?", "source_prompt": "{table}\n{question}", "answer_dp": ["construction", "mining, quarrying and oil and gas extraction", "public administration"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_1837_10375_1", "source_table_id": "1837"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2421_12958_1", "source": "hitab", "table_key": "677bc9eae226d2928b822a8e3c21f66fc2b6445e", "grid": {"TD": 1, "LD": 3, "nrows": 63, "ncols": 13, "section_rows": [2, 23, 34, 43, 47, 56], "cells": [["", "", "", "n.l", "p.e.i", "n.s", "n.b", "que", "ont", "man", "sask", "alta", "b.c"], ["rate", "total criminal code offences (excluding traffic) - crime rate", "", "5765", "3549", "4696", "4373", "3377", "3703", "11309", "13829", "10964", "6418"], ["rate", "total violent crime", "", "1228", "869", "1194", "1203", "1314", "987", "2933", "3118", "1870", "1367"], ["rate", "total violent crime", "homicide", "0.4", "0", "2.2", "2.5", "1.5", "1.5", "4.4", "4.9", "3.4", "2.2"], ["rate", "total violent crime", "other offences causing death", "0", "0", "0.3", "0", "0.5", "0.1", "0.9", "1.2", "0", "0.9"], ["rate", "total violent crime", "attempted murder", "0", "0", "2.5", "2.2", "2.7", "0.7", "2.8", "2.6", "2", "2.2"], ["rate", "total violent crime", "sexual assault", "1.6", "0", "1.2", "1.1", "1.6", "1.9", "2.8", "2.1", "2.2", "0.9"], ["rate", "total violent crime", "sexual assault", "60", "47", "65", "53", "83", "78", "116", "109", "75", "71"], ["rate", "total violent crime", "sexual violations against children", "27", "21", "30", "24", "44", "30", "55", "58", "35", "32"], ["rate", "total violent crime", "assault (level 3) - aggravated", "10", "6", "8", "4", "5", "13", "59", "42", "24", "12"], ["rate", "total violent crime", "assault (level 2) - weapon or bodily harm", "126", "98", "106", "111", "158", "110", "547", "509", "267", "148"], ["rate", "total violent crime", "assault", "558", "384", "486", "493", "556", "453", "1432", "1502", "836", "589"], ["rate", "total violent crime", "assault against a peace officer", "23", "6", "22", "43", "53", "27", "82", "63", "44", "31"], ["rate", "total violent crime", "other assaults", "1", "0", "1", "3", "7", "3", "4", "8", "4", "8"], ["rate", "total violent crime", "firearms - use of, discharge, pointing", "6", "2", "7", "10", "7", "3", "25", "45", "31", "9"], ["rate", "total violent crime", "robbery", "14", "0", "12", "13", "10", "12", "35", "46", "32", "14"], ["rate", "total violent crime", "forcible confinement or kidnapping", "10", "2", "10", "7", "13", "11", "19", "19", "16", "11"], ["rate", "total violent crime", "extortion", "5", "4", "12", "6", "9", "3", "2", "10", "9", "4"], ["rate", "total violent crime", "criminal harassment", "28", "38", "69", "57", "78", "74", "26", "67", "65", "50"], ["rate", "total violent crime", "uttering threats", "266", "162", "227", "293", "236", "137", "396", "456", "305", "243"], ["rate", "total violent crime", "indecent or harassing communications", "72", "90", "112", "61", "15", "14", "101", "142", "103", "126"], ["rate", "total violent crime", "non-consensual distribution of intimate images", "7", "0", "14", "8", "2", "4", "3", "9", "3", "2"], ["rate", "total violent crime", "other violent criminal code offences", "12", "11", "8", "14", "30", "12", "17", "21", "14", "11"], ["rate", "total property crime", "", "3141", "2145", "2687", "2441", "1544", "2092", "5405", "7179", "6858", "3293"], ["rate", "total property crime", "breaking and entering", "414", "218", "325", "468", "420", "363", "819", "862", "978", "443"], ["rate", "total property crime", "possession of stolen property", "17", "8", "27", "35", "22", "32", "48", "182", "500", "62"], ["rate", "total property crime", "motor vehicle theft", "87", "60", "103", "182", "171", "123", "323", "487", "747", "197"], ["rate", "total property crime", "theft over $5,000", "19", "15", "26", "29", "37", "39", "43", "58", "114", "68"], ["rate", "total property crime", "theft $5,000 or under", "544", "544", "775", "597", "380", "712", "900", "1071", "1746", "1118"], ["rate", "total property crime", "fraud", "158", "169", "553", "346", "120", "264", "244", "491", "387", "238"], ["rate", "total property crime", "identity fraud", "13", "15", "7", "9", "16", "1", "4", "11", "10", "6"], ["rate", "total property crime", "identity theft", "14", "15", "19", "19", "12", "5", "20", "29", "40", "26"], ["rate", "total property crime", "mischief", "1843", "1056", "813", "691", "326", "543", "2938", "3894", "2282", "1111"], ["rate", "total property crime", "arson", "32", "45", "38", "65", "39", "11", "67", "94", "55", "24"], ["rate", "total other criminal code offences", "", "1397", "534", "816", "728", "518", "624", "2971", "3532", "2236", "1758"], ["rate", "total other criminal code offences", "weapon violations", "31", "13", "35", "37", "33", "34", "101", "115", "88", "47"], ["rate", "total other criminal code offences", "child pornography", "19", "11", "42", "4", "7", "5", "11", "10", "8", "27"], ["rate", "total other criminal code offences", "disturbing the peace", "690", "228", "242", "171", "29", "88", "1535", "1090", "906", "962"], ["rate", "total other criminal code offences", "failure to comply with order", "407", "98", "276", "246", "281", "246", "682", "1219", "717", "290"], ["rate", "total other criminal code offences", "failure to appear", "18", "8", "6", "27", "15", "49", "12", "353", "180", "77"], ["rate", "total other criminal code offences", "breach of probation", "73", "92", "75", "104", "89", "113", "368", "393", "84", "172"], ["rate", "total other criminal code offences", "other offences against the administration of law and justice", "21", "23", "22", "24", "18", "27", "122", "84", "74", "32"], ["rate", "total other criminal code offences", "other violations", "137", "62", "118", "116", "46", "62", "140", "268", "179", "151"], ["rate", "total criminal code traffic violations", "", "256", "374", "416", "381", "616", "247", "718", "1168", "796", "484"], ["rate", "total criminal code traffic violations", "alcohol-impaired driving", "180", "278", "307", "258", "232", "180", "571", "908", "572", "392"], ["rate", "total criminal code traffic violations", "drug-impaired driving", "12", "17", "22", "15", "12", "7", "17", "31", "25", "17"], ["rate", "total criminal code traffic violations", "other criminal code traffic violations", "64", "79", "88", "108", "372", "60", "130", "229", "199", "75"], ["rate", "total drug offences", "", "229", "166", "345", "277", "311", "202", "346", "339", "392", "482"], ["rate", "total drug offences", "possession - cannabis", "120", "75", "164", "152", "141", "92", "115", "115", "171", "316"], ["rate", "total drug offences", "possession - cocaine", "13", "6", "16", "12", "12", "12", "20", "17", "34", "33"], ["rate", "total drug offences", "possession - methamphetamines or ecstasy", "0", "4", "3", "16", "33", "12", "14", "18", "39", "21"], ["rate", "total drug offences", "possession - other drugs", "12", "15", "15", "16", "9", "31", "16", "13", "24", "44"], ["rate", "total drug offences", "trafficking, production or distribution - cannabis", "26", "30", "64", "37", "77", "18", "51", "47", "29", "40"], ["rate", "total drug offences", "trafficking, production or distribution - cocaine", "34", "17", "50", "18", "13", "10", "73", "74", "48", "12"], ["rate", "total drug offences", "trafficking, production or distribution - methamphetamines or ecstasy", "0", "4", "2", "7", "17", "2", "25", "25", "21", "5"], ["rate", "total drug offences", "trafficking, production or distribution - other drugs", "23", "15", "30", "19", "9", "24", "32", "29", "26", "12"], ["rate", "total other federal statute violations", "", "53", "28", "23", "45", "622", "57", "196", "622", "80", "246"], ["rate", "total other federal statute violations", "youth criminal justice act", "1", "4", "6", "5", "3", "8", "44", "97", "11", "18"], ["rate", "total other federal statute violations", "firearms act", "4", "17", "1", "9", "1", "1", "14", "36", "25", "30"], ["rate", "total other federal statute violations", "customs act", "5", "0", "0", "7", "615", "2", "80", "9", "4", "3"], ["rate", "total other federal statute violations", "canada shipping act", "2", "0", "1", "0", "1", "38", "38", "4", "0", "146"], ["rate", "total other federal statute violations", "other federal statutes", "42", "8", "15", "23", "2", "8", "21", "476", "39", "49"], ["rate", "total - all violations", "", "6304", "4117", "5480", "5075", "4926", "4208", "12568", "15958", "12233", "7630"]]}, "native_split": "test", "question": "list the top 2 provinces with the lowest rural sexual assault rates.", "source_prompt": "{table}\n{question}", "answer_dp": ["p.e.i", "n.b"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_2421_12958_1", "source_table_id": "2421"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_920_5307_1", "source": "hitab", "table_key": "b657592cd496b02a7546cec2d2ade3cac82efc77", "grid": {"TD": 3, "LD": 2, "nrows": 39, "ncols": 8, "section_rows": [3, 15, 27], "cells": [["", "", "women", "women", "women", "men", "men", "men"], ["", "", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to", "", "from", "to"], ["physical assault", "", "", "", "", "", "", ""], ["physical assault", "newfoundland and labrador", "21.5", "18.5", "24.8", "26.3", "22.7", "30.3"], ["physical assault", "prince edward island", "30.3", "26", "35", "29.6", "24.5", "35.2"], ["physical assault", "nova scotia", "28", "25.3", "31", "39.7", "36.2", "43.4"], ["physical assault", "new brunswick", "23.9", "21.2", "26.8", "30.1", "26.9", "33.5"], ["physical assault", "quebec", "20.8", "19.2", "22.6", "26.1", "24.3", "28"], ["physical assault", "ontario", "26.6", "24.7", "28.6", "33.4", "31.3", "35.6"], ["physical assault", "manitoba", "28.6", "26", "31.3", "36.5", "33.6", "39.6"], ["physical assault", "saskatchewan", "25.6", "23.7", "27.7", "36.5", "33.9", "39.3"], ["physical assault", "alberta", "30.5", "28.2", "32.8", "39.1", "36.6", "41.5"], ["physical assault", "british columbia", "29.5", "27.4", "31.6", "38.7", "36.3", "41.2"], ["physical assault", "provinces", "26.1", "25.2", "27.1", "33.3", "32.2", "34.4"], ["sexual assault", "", "", "", "", "", "", ""], ["sexual assault", "newfoundland and labrador", "25.4", "22.3", "29", "8.2", "6.1", "10.9"], ["sexual assault", "prince edward island", "34.6", "30.2", "39", "6.1", "4", "9.3"], ["sexual assault", "nova scotia", "33.1", "30.3", "36", "9.6", "7.8", "11.8"], ["sexual assault", "new brunswick", "28.5", "25.6", "32", "9.8", "7.8", "12.3"], ["sexual assault", "quebec", "24.5", "22.8", "26", "5.7", "4.9", "6.6"], ["sexual assault", "ontario", "29.8", "27.9", "32", "8.2", "7.1", "9.4"], ["sexual assault", "manitoba", "31.7", "29.2", "34", "8.9", "7.3", "10.9"], ["sexual assault", "saskatchewan", "30.9", "28.8", "33", "7.9", "6.5", "9.5"], ["sexual assault", "alberta", "35.3", "32.9", "38", "9.4", "8", "11"], ["sexual assault", "british columbia", "36.5", "34.4", "39", "11.1", "9.6", "12.7"], ["sexual assault", "provinces", "30.2", "29.3", "31", "8.2", "7.7", "8.8"], ["total violent victimization", "", "", "", "", "", "", ""], ["total violent victimization", "newfoundland and labrador", "32.3", "29", "35.8", "28.4", "24.7", "32.3"], ["total violent victimization", "prince edward island", "42.9", "38.3", "47.5", "30.9", "25.8", "36.6"], ["total violent victimization", "nova scotia", "41.6", "38.6", "44.7", "41.9", "38.3", "45.6"], ["total violent victimization", "new brunswick", "37.8", "34.6", "41", "32.5", "29.3", "35.9"], ["total violent victimization", "quebec", "31.7", "29.9", "33.6", "28.2", "26.3", "30.1"], ["total violent victimization", "ontario", "38.9", "36.9", "41", "35.5", "33.3", "37.8"], ["total violent victimization", "manitoba", "41.5", "38.8", "44.2", "39.1", "36.1", "42.2"], ["total violent victimization", "saskatchewan", "38.4", "36.2", "40.7", "37.9", "35.2", "40.6"], ["total violent victimization", "alberta", "44.1", "41.6", "46.6", "40.7", "38.3", "43.2"], ["total violent victimization", "british columbia", "44.5", "42.3", "46.8", "40.9", "38.5", "43.4"], ["total violent victimization", "provinces", "38.7", "37.7", "39.7", "35.3", "34.3", "36.4"]]}, "native_split": "test", "question": "for women who were 15 or older, which two provinces where the likelihood of experiencing physical assault were highest?", "source_prompt": "{table}\n{question}", "answer_dp": ["alberta", "british columbia"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_920_5307_1", "source_table_id": "920"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1473_8059_1", "source": "hitab", "table_key": "02487b18831093fac25152754e60025353985c8b", "grid": {"TD": 1, "LD": 2, "nrows": 3, "ncols": 7, "section_rows": [], "cells": [["", "", "lowest quartile", "second quartile", "third quartile", "highest quartile", "total"], ["percent", "mobile devices only", "24.1", "14.7", "13.8", "8", "13.5"], ["percent", "personal computer", "75.9", "85.3", "86.2", "92", "86.5"]]}, "native_split": "test", "question": "who were more likely to use only mobile devices to access the internet,households in the second and third income quartiles or households in the highest quartile?", "source_prompt": "{table}\n{question}", "answer_dp": ["second quartile", "third quartile"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_1473_8059_1", "source_table_id": "1473"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_2_totto293-2_0_1", "source": "hitab", "table_key": "c6243944daeef6eb9ea30768f77e7a41b9ca4102", "grid": {"TD": 2, "LD": 4, "nrows": 32, "ncols": 14, "section_rows": [], "cells": [["", "", "", "", "total", "total", "tajik league", "tajik league", "tajik cup", "tajik cup", "super cup", "super cup", "afc cup", "afc cup"], ["", "", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["1", "gk", "serbia", "nikola stosic", "33", "0", "20", "0", "6", "0", "1", "0", "6", "0"], ["2", "df", "tajikistan", "siyovush asrorov", "25", "1", "15+1", "0", "4", "1", "1", "0", "4", "0"], ["3", "df", "tajikistan", "tabrezi davlatmir", "19", "0", "11+1", "0", "4", "0", "0+1", "0", "2", "0"], ["5", "df", "ukraine", "artem baranovskyi", "25", "1", "12+2", "0", "3+1", "1", "1", "0", "6", "0"], ["7", "mf", "uzbekistan", "jahongir abdumominov", "14", "4", "8", "0", "5+1", "4", "0", "0", "0", "0"], ["11", "mf", "tajikistan", "muhammadjon rakhimov", "26", "2", "10+6", "2", "4", "0", "1", "0", "2+3", "0"], ["13", "mf", "tajikistan", "nozim babadjanov", "32", "4", "6+12", "1", "5+2", "2", "0+1", "1", "4+2", "0"], ["14", "fw", "tajikistan", "sheriddin boboev", "29", "16", "15+5", "12", "3+1", "4", "0+1", "0", "1+3", "0"], ["17", "fw", "tajikistan", "dilshod vasiev", "34", "11", "10+11", "6", "6+1", "4", "1", "0", "1+4", "1"], ["18", "mf", "tajikistan", "fatkhullo fatkhuloev", "31", "9", "18+1", "4", "5", "2", "1", "0", "6", "3"], ["19", "df", "tajikistan", "akhtam nazarov", "35", "6", "19+2", "2", "7", "2", "1", "0", "6", "2"], ["20", "mf", "tajikistan", "amirbek juraboev", "33", "1", "19+1", "1", "7", "0", "1", "0", "5", "0"], ["23", "mf", "tajikistan", "ehson panjshanbe", "24", "1", "15+1", "1", "2", "0", "0", "0", "4+2", "0"], ["27", "df", "tajikistan", "alisher sharipov", "5", "0", "4", "0", "1", "0", "0", "0", "0", "0"], ["28", "fw", "tajikistan", "komron tursunov", "24", "4", "9+7", "2", "5+1", "2", "0+1", "0", "0+1", "0"], ["35", "gk", "russia", "rustam yatimov", "3", "0", "1", "0", "1+1", "0", "0", "0", "0", "0"], ["70", "mf", "tajikistan", "shahrom sulaimonov", "17", "0", "10", "0", "7", "0", "0", "0", "0", "0"], ["youth team players:", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["none", "df", "tajikistan", "amir nigmatov", "2", "0", "0", "0", "0+2", "0", "0", "0", "0", "0"], ["none", "df", "tajikistan", "shahbol rajabov", "1", "0", "1", "0", "0", "0", "0", "0", "0", "0"], ["none", "df", "tajikistan", "khurshedi zainiddin", "2", "0", "1", "0", "1", "0", "0", "0", "0", "0"], ["none", "mf", "tajikistan", "farhod kosimov", "3", "0", "1+1", "0", "1", "0", "0", "0", "0", "0"], ["none", "fw", "tajikistan", "hamid golami", "1", "0", "0+1", "0", "0", "0", "0", "0", "0", "0"], ["players away from istiklol on loan:", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["players who left istiklol during the season:", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["6", "df", "tajikistan", "davron erghashev", "11", "0", "5", "0", "0", "0", "0", "0", "5+1", "0"], ["7", "mf", "belarus", "sergey tikhonovsky", "6", "0", "2+1", "0", "0", "0", "1", "0", "2", "0"], ["9", "fw", "belarus", "mikalay zyanko", "15", "5", "7+1", "2", "0", "0", "1", "1", "6", "2"], ["21", "mf", "tajikistan", "romish jalilov", "16", "3", "9", "2", "0", "0", "1", "0", "6", "1"], ["33", "df", "ukraine", "oleksandr garbar", "2", "0", "2", "0", "0", "0", "0", "0", "0", "0"]]}, "native_split": "test", "question": "which cup or league did fc istiklol season participate in?", "source_prompt": "{table}\n{question}", "answer_dp": ["tajik league", "tajik cup", "super cup", "afc cup"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "set_match", "meta": {"source_local_id": "hitab_2_totto293-2_0_1", "source_table_id": "2_totto293-2"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_248_1360_1", "source": "hitab", "table_key": "39811ebc3c1b239f48b62aa20cd3f9e683c72de2", "grid": {"TD": 2, "LD": 1, "nrows": 10, "ncols": 8, "section_rows": [], "cells": [["", "number of syrian refugees", "number of refugees from other countries", "distribution of syrian refugees", "distribution of refugees from other countries", "proportion of syrian refugees in the total refugee population", "proportion of syrian refugees who were government-assisted refugees", "proportion of syrian refugees admitted in 2015"], ["", "number", "number", "percent", "percent", "percent", "percent", "percent"], ["atlantic provinces", "2670", "765", "10.7", "4.9", "77.7", "85.8", "6.6"], ["montreal census metropolitan area", "4265", "1195", "17", "7.7", "78.1", "5.4", "46.5"], ["rest of quebec", "1030", "1970", "4.1", "12.6", "34.3", "76.7", "5.3"], ["toronto census metropolitan area", "4035", "3285", "16.1", "21.1", "55.1", "30.9", "37.4"], ["rest of ontario", "6175", "2910", "24.6", "18.7", "68", "65.3", "27.5"], ["prairies", "4735", "4055", "18.9", "26", "53.9", "64.8", "24"], ["british columbia", "2125", "1410", "8.5", "9.1", "60.1", "73.6", "29.4"], ["canada", "25035", "15580", "100", "100", "61.6", "52.8", "28.7"]]}, "native_split": "test", "question": "what percentage of the total refugee population living in the rest of ontario and prairies were syrian refugees?", "source_prompt": "{table}\n{question}", "answer_dp": ["68", "53.9"], "answer_type": "multi-span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_248_1360_1", "source_table_id": "248"}, "dataset": "hitab", "subtype": "multi-span"}
{"id": "hitab:hitab_1312_7363_1(3)", "source": "hitab", "table_key": "cb68e88192a5d0b42d0b86578772083da1b3ef29", "grid": {"TD": 2, "LD": 2, "nrows": 47, "ncols": 11, "section_rows": [3, 8, 22, 25, 28, 32, 40, 43], "cells": [["", "", "in-scope population", "in-scope population", "linkage rate", "birth-census cohort", "birth-census cohort", "birth-census cohort", "birth-census cohort", "ratio", "ratio"], ["", "", "number", "% distribution", "%", "number", "% distribution", "number", "% distribution", "", ""], ["total", "", "687340", "100", "90.3", "135426", "100", "687340", "100", "1", "1"], ["type of birth", "", "", "", "", "", "", "", "", "", ""], ["type of birth", "surviving to age 1", "679431", "98.8", "90.5", "134002", "98.9", "679399", "98.8", "1", "1"], ["type of birth", "stillbirth", "4240", "0.6", "76.3", "808", "0.6", "4242", "0.6", "0.97", "1"], ["type of birth", "infant death", "2984", "0.4", "79.8", "616", "0.5", "3699", "0.5", "1.05", "1.01"], ["type of birth", "infant death", "685", "0.1", "0", "0", "0", "0", "0", "", ""], ["province/territory of birth", "", "", "", "", "", "", "", "", "", ""], ["province/territory of birth", "newfoundland and labrador", "9111", "1.3", "91.6", "1794", "1.3", "9111", "1.3", "1", "1"], ["province/territory of birth", "prince edward island", "2664", "0.4", "91.2", "457", "0.3", "2664", "0.4", "0.87", "1"], ["province/territory of birth", "nova scotia", "17345", "2.5", "91", "3307", "2.4", "17345", "2.5", "0.97", "1"], ["province/territory of birth", "new brunswick", "13966", "2", "92.1", "2777", "2.1", "13966", "2", "1.01", "1"], ["province/territory of birth", "quebec", "151307", "22", "92.8", "29250", "21.6", "151307", "22", "0.98", "1"], ["province/territory of birth", "ontario", "270086", "39.3", "90", "48638", "35.9", "270086", "39.3", "0.91", "1"], ["province/territory of birth", "manitoba", "29247", "4.3", "90.3", "8584", "6.3", "29247", "4.3", "1.49", "1"], ["province/territory of birth", "saskatchewan", "24367", "3.5", "89.1", "6458", "4.8", "24367", "3.5", "1.35", "1"], ["province/territory of birth", "alberta", "84367", "12.3", "89.7", "17424", "12.9", "84367", "12.3", "1.05", "1"], ["province/territory of birth", "british columbia", "81970", "11.9", "86.9", "15026", "11.1", "81970", "11.9", "0.93", "1"], ["province/territory of birth", "yukon", "685", "0.1", "91.1", "266", "0.2", "685", "0.1", "1.97", "1"], ["province/territory of birth", "northwest territories", "1452", "0.2", "87.3", "825", "0.6", "1452", "0.2", "2.88", "1"], ["province/territory of birth", "nunavut", "773", "0.1", "82.1", "620", "0.5", "773", "0.1", "4.07", "1"], ["postal code at birth", "", "", "", "", "", "", "", "", "", ""], ["postal code at birth", "urban", "563406", "82", "90.4", "101496", "74.9", "562915", "81.9", "0.91", "1"], ["postal code at birth", "rural", "120245", "17.5", "90.6", "32970", "24.3", "121907", "17.7", "1.39", "1.01"], ["sex of child", "", "", "", "", "", "", "", "", "", ""], ["sex of child", "male", "352849", "51.3", "90.2", "69325", "51.2", "352779", "51.3", "1", "1"], ["sex of child", "female", "334432", "48.7", "90.4", "66089", "48.8", "334501", "48.7", "1", "1"], ["year of birth", "", "", "", "", "", "", "", "", "", ""], ["year of birth", "2004", "214647", "31.2", "89.8", "42222", "31.2", "214647", "31.2", "1", "1"], ["year of birth", "2005", "344654", "50.1", "90.3", "67670", "50", "344654", "50.1", "1", "1"], ["year of birth", "2006", "128039", "18.6", "91", "25534", "18.9", "128039", "18.6", "1.01", "1"], ["maternal age group", "", "", "", "", "", "", "", "", "", ""], ["maternal age group", "younger than 20", "28203", "4.1", "77.3", "6736", "5", "28218", "4.1", "1.21", "1"], ["maternal age group", "20 to 24", "110969", "16.1", "85.1", "22460", "16.6", "111082", "16.2", "1.03", "1"], ["maternal age group", "25 to 29", "211655", "30.8", "91", "41381", "30.6", "211889", "30.8", "0.99", "1"], ["maternal age group", "30 to 34", "215677", "31.4", "92.8", "41603", 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["female", "unintentional injury", "", "3.06", "1.17", "-61.8", "0", "-4.9", "-6", "-3.9", "0.77", "-34", "-9.9", "-27.1", "0.9"], ["female", "unintentional injury", "transport vehicle", "3.63", "0.51", "-86.1", "0.001", "-9.9", "-12.1", "-7.5", "0.14", "-72.6", "-27.6", "", "2.8"], ["female", "unintentional injury", "medical/surgical mishap", "2.27", "1.07", "-53", "0.597", "-3.9", "-6", "-1.8", "1.09", "1.7", "0.4", "-9.3", "7.9"], ["female", "unintentional injury", "fall", "3.21", "1.35", "-57.9", "0.005", "-4.5", "-6.1", "-2.8", "0.96", "-29", "-8.2", "-35", "5.6"], ["female", "self-inflicted injury", "", "5.15", "1.79", "-65.2", "0.224", "-5.4", "-8.2", "-2.5", "1.55", "-13.3", "-3.5", "-17.9", "6.4"], ["female", "self-inflicted injury", "poisoning", "4.96", "1.86", "-62.4", "0.293", "-5", "-7.9", "-2", "1.58", "-15.1", "-4", "-18.5", "5.9"], ["female", "intentionally inflicted by another", "", "10.86", "5.76", "-47", "0.814", "-3.3", "-9.4", "3.3", "5.27", "-8.5", "-2.2", "-18.6", "8.6"], ["total british columbia", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["total british columbia", "unintentional injury", "", "1.44", "0.73", "-49.3", "", "-3.5", "-3.6", "-3.4", "0.58", "-20.5", "-5.6", "-11.8", "-0.4"], ["total british columbia", "unintentional injury", "transport vehicle", "1.67", "0.52", "-68.7", "", "-5.9", "-6.2", "-5.6", "0.28", "-46.2", "-14.4", "-53.7", "0.7"], ["total british columbia", "unintentional injury", "medical/surgical mishap", "1.35", "0.72", "-46.4", "", "-3.2", "-3.5", "-3", "0.59", "-18.2", "-4.9", "-16.1", "3.3"], ["total british columbia", "unintentional injury", "fall", "1.28", "0.83", "-34.8", "", "-2.2", "-2.4", "-2", "0.74", "-11.3", "-2.9", "-6.8", "0.5"], ["total british columbia", "self-inflicted injury", "", "1.37", "0.65", "-52.4", "", "-3.8", "-4.3", "-3.3", "0.49", "-25.5", "-7.1", "-13.3", "-1.9"], ["total british columbia", "self-inflicted injury", "poisoning", "1.33", "0.66", "-50.2", "", "-3.6", "-4.1", "-3.1", "0.51", "-22.9", "-6.3", "-12.3", "-1.3"], ["total british columbia", "intentionally inflicted by another", "", "1.35", "0.8", "-40.7", "", "-2.7", "-3.3", "-2.1", "0.69", "-14.4", "-3.8", "-12.9", "3.2"], ["male", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["male", "unintentional injury", "", "1.5", "0.7", "-53.7", "", "-4", "-4.1", "-3.8", "0.53", "-24.4", "-6.7", "-14.8", "-0.4"], ["male", "unintentional injury", "transport vehicle", "1.63", "0.5", "-69.3", "", "-6", "-6.4", "-5.7", "0.26", "-47.5", "-14.9", "", "2.5"], ["male", "unintentional injury", "medical/surgical mishap", "1.37", "0.75", "-45.3", "", "-3.1", "-3.5", "-2.8", "0.62", "-17.5", "-4.7", "-16", "3.6"], ["male", "unintentional injury", "fall", "1.32", "0.81", "-38.5", "", "-2.5", "-2.8", "-2.2", "0.71", "-13.2", "-3.5", "-9", "1.3"], ["male", "self-inflicted injury", "", "1.37", "0.6", "-56.4", "", "-4.3", "-5.1", "-3.4", "0.45", "-24.6", "-6.8", "-14", "-0.9"], ["male", "self-inflicted injury", "poisoning", "1.28", "0.59", "-54.4", "", "-4", "-5", "-3.1", "0.5", "-14.6", "-3.9", "-11.6", "2.3"], ["male", "intentionally inflicted by another", "", "1.25", "0.79", "-36.5", "", "-2.4", "-3", "-1.7", "0.7", "-12.1", "-3.2", "-12.3", "4"], ["female", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["female", "unintentional injury", "", "1.37", "0.77", "-43.8", "", "-3", "-3.2", "-2.8", "0.64", "-16.4", "-4.4", "-9.8", "0.3"], ["female", "unintentional injury", "transport vehicle", "1.72", "0.56", "-67.7", "", "-5.8", "-6.3", "-5.3", "0.31", "-44", "-13.5", "-42.5", "0.2"], ["female", "unintentional injury", "medical/surgical mishap", "1.33", "0.7", "-47.4", "", "-3.3", "-3.7", "-3", "0.57", "-19", "-5.1", "-17.7", "3.8"], ["female", "unintentional injury", "fall", "1.25", "0.85", "-31.7", "", "-2", "-2.2", "-1.7", "0.77", "-9.8", "-2.5", "-6.3", "0.8"], ["female", "self-inflicted injury", "", "1.37", "0.68", "-50", "", "-3.6", "-4.2", "-2.9", "0.51", "-26", "-7.2", "-14.7", "-1.3"], ["female", "self-inflicted injury", "poisoning", "1.35", "0.7", "-48", "", "-3.4", "-4", "-2.7", "0.52", "-26.3", "-7.4", "-14.5", "-1.6"], ["female", "intentionally inflicted by another", "", "1.85", "0.84", "-54.5", "", "-4.1", "-5.6", "-2.5", "0.94", "11.8", "2.8", "-15.4", "14.6"]]}, "native_split": "test", "question": "what was the percent change of the srr of injury hospitalization for unintentional transportation vehicle collisions among aboriginal population?", "source_prompt": "{table}\n{question}", "answer_dp": "-84.1", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2441_13036_1", "source_table_id": "2441"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_549_3071_2", "source": "hitab", "table_key": "c0b1d2dfb49a02666237f1cfc291b390e46edc3f", "grid": {"TD": 2, "LD": 2, "nrows": 6, "ncols": 6, "section_rows": [], "cells": [["", "", "firm structure", "firm structure", "firm structure", "firm structure"], ["", "", "total", "single", "multiple", "multiple province"], ["percent", "goods only", "100", "98.6", "1.4", "33.3"], ["percent", "services only", "100", "91.2", "8.8", "65.7"], ["percent", "hybrid goods", "100", "", "100", "85.2"], ["percent", "total business sector", "100", "90.9", "9.1", "69.3"]]}, "native_split": "test", "question": "what are the percentage of the shares for the goods-only?", "source_prompt": "{table}\n{question}", "answer_dp": "33.3", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_549_3071_2", "source_table_id": "549"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_211_1125_2", "source": "hitab", "table_key": "dde1ae2b21b19cc08bb09e9c50c10ded462d73e2", "grid": {"TD": 2, "LD": 2, "nrows": 13, "ncols": 8, "section_rows": [], "cells": [["", "", "imports", "imports", "imports", "exports", "exports", "exports"], ["", "", "all firms", "immigrant-owned firms", "canadian-owned firms", "all firms", "immigrant-owned firms", "canadian-owned firms"], ["percent", "agriculture, forestry, fishing and hunting", "1.05", "1.25", "1.03", "4.1", "2.39", "4.38"], ["percent", "mining, oil and gas extraction, and utilities", "0.44", "0.55", "0.43", "0.95", "1.74", "0.82"], ["percent", "construction", "2.14", "1.06", "2.27", "1.9", "0.61", "2.11"], ["percent", "manufacturing", "25.99", "20.3", "26.67", "55.34", "39.33", "57.91"], ["percent", "wholesale trade", "51.18", "60.45", "50.06", "21.06", "39.74", "18.05"], ["percent", "retail trade", "10.72", "8.84", "10.94", "1.53", "5.98", "0.81"], ["percent", "transportation and warehousing", "2.05", "1.5", "2.12", "2.59", "2.6", "2.59"], ["percent", "professional services", "5.05", "4.52", "5.12", "11.8", "6.91", "12.58"], ["percent", "education and health services", "0.13", "0.19", "0.12", "0.06", "0.12", "0.05"], ["percent", "arts, entertainment and recreation, and accommodation and food services", "0.3", "0.37", "0.29", "0.22", "0.27", "0.21"], ["percent", "other services", "0.96", "0.97", "0.95", "0.45", "0.32", "0.47"]]}, "native_split": "test", "question": "what percentage of exports that manufacturing and wholesale trade accounting for?", "source_prompt": "{table}\n{question}", "answer_dp": "76.4", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_211_1125_2", "source_table_id": "211"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_112_486_1", "source": "hitab", "table_key": "6ea27e60c3cc43dbf0a38160b8253f6f89461440", "grid": {"TD": 2, "LD": 2, "nrows": 10, "ncols": 9, "section_rows": [2], "cells": [["", "", "area of agricultural land", "cropland", "cropland", "natural land for pasture", "natural land for pasture", "woodlands and wetlands", "woodlands and wetlands"], ["", "", "hectares", "hectares", "percent", "hectares", "percent", "hectares", "percent"], ["canada", "", "64812723", "35350270", "54.5", "14703330", "22.7", "4897367", "7.6"], ["canada", "atlantic provinces", "1063343", "430363", "40.5", "65711", "6.2", "450031", "42.3"], ["canada", "quebec", "3341333", "1874760", "56.1", "134147", "4", "1057417", "31.6"], ["canada", "ontario", "5126653", "3613821", "70.5", "398538", "7.8", "646578", "12.6"], ["canada", "manitoba", "7293839", "4348869", "59.6", "1466968", "20.1", "549444", "7.5"], ["canada", "saskatchewan", "24940023", "14728934", "59.1", "4816782", "19.3", "1009381", "4"], ["canada", "alberta", "20436150", "9753849", "47.7", "6435825", "31.5", "893436", "4.4"], ["canada", "british columbia", "2611382", "599674", "23", "1385359", "53.1", "291079", "11.1"]]}, "native_split": "test", "question": "what was the percentage of agricultural land in the atlantic provinces reported as woodlands and wetlands in 2011?", "source_prompt": "{table}\n{question}", "answer_dp": "42.3", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_112_486_1", "source_table_id": "112"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_243_totto27608-2_0_1", "source": "hitab", "table_key": "3b317f5b8a92f4ed4b913c0f3fbb214fe6ffe48d", "grid": {"TD": 2, "LD": 2, "nrows": 7, "ncols": 14, "section_rows": [], "cells": [["", "", "passing", "passing", "passing", "passing", "passing", "passing", "passing", "passing", "rushing", "rushing", "rushing", "rushing"], ["", "", "cmp", "att", "pct", "yds", "y/a", "td", "int", "rtg", "att", "yds", "avg", "td"], ["2009", "miami", "247", "401", "61.6", "2611", "6.5", "12", "16", "118.2", "149", "258", "1.7", "5"], ["2010", "miami", "222", "343", "64.7", "2406", "7", "13", "12", "129.2", "79", "27", "0.3", "1"], ["2011", "miami", "295", "448", "65.8", "3513", "7.8", "23", "11", "143.7", "125", "115", "0.9", "4"], ["2012", "miami", "302", "480", "62.9", "3483", "7.3", "25", "12", "136.1", "108", "265", "2.5", "2"], ["career", "", "1066", "1672", "63.8", "12013", "7.2", "73", "51", "132.4", "461", "665", "1.4", "12"]]}, "native_split": "train", "question": "how many passing yards did zac dysert finish his sophomore season with?", "source_prompt": "{table}\n{question}", "answer_dp": "2406", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_243_totto27608-2_0_1", "source_table_id": "243_totto27608-2"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2769_15078_1", "source": "hitab", "table_key": "6f55297a5c68960ef800c684b4c78dbad97fd87b", "grid": {"TD": 1, "LD": 2, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["", "", "yukon", "northwest territories", "nunavut", "all territories"], ["percent", "crime was minor and not worth taking the time to report", "81", "82", "70", "78"], ["percent", "police wouldn't have considered the incident important enough", "74", "67", "62", "68"], ["percent", "incident was a private or personal matter and was handled informally", "48", "51", "51", "50"], ["percent", "no one was harmed/no financial loss", "53", "49", "46", "49"], ["percent", "lack of evidence", "45", "53", "42", "47"], ["percent", "police wouldn't have found property/offender", "46", "50", "41", "46"], ["percent", "did not want the hassle of dealing with the police", "38", "48", "40", "42"], ["percent", "police would not have been effective", "36", "38", "39", "37"], ["percent", "offender would not be convicted or adequately punished", "31", "42", "40", "38"], ["percent", "no harm was intended", "33", "37", "34", "35"], ["percent", "feared or did not want the hassle of dealing with the court process", "20", "33", "", "25"], ["percent", "did not want to get the offender in trouble", "", "", "", "15"], ["percent", "unsatisfactory service (from police) in the past", "15", "18", "10", "14"], ["percent", "didn't want others to know", "", "", "", "8"], ["percent", "reported to another official", "", "", "", "8"], ["percent", "police would be biased", "", "", "", "7"], ["percent", "insurance wouldn't cover it", "", "", "", "7"], ["percent", "nothing was taken/all items were recovered", "", "", "", "6"], ["percent", "did not need a police report to file claim", "", "", "", "4"]]}, "native_split": "test", "question": "what is the proportion in nunavut in which the reason for not reporting an incident to the police was that it was too minor to be worth reporting?", "source_prompt": "{table}\n{question}", "answer_dp": "70", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2769_15078_1", "source_table_id": "2769"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2543_13593_1", "source": "hitab", "table_key": "1a53261f07a0876f38c25ffcf92fd02dbefb52af", "grid": {"TD": 2, "LD": 2, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["", "", "female", "female", "male", "male"], ["", "", "with transfers", "without transfers", "with transfers", "without transfers"], ["percent", "0 to 4 years", "18.9", "35.2", "16.6", "32.8"], ["percent", "5 to 9 years", "13.2", "31", "12.8", "27.8"], ["percent", "10 to 14 years", "12.9", "27", "11.9", "26.5"], ["percent", "15 to 19 years", "13.9", "25.7", "14.3", "25.6"], ["percent", "20 to 24 years", "15.9", "24", "11.5", "18"], ["percent", "25 to 29 years", "13", "20.9", "9.8", "17.5"], ["percent", "30 to 34 years", "11.7", "21.9", "11.4", "19.7"], ["percent", "35 to 39 years", "12.5", "23.3", "10.8", "20.7"], ["percent", "40 to 44 years", "12", "22.2", "10.1", "19.4"], ["percent", "45 to 49 years", "11.5", "21.3", "10.4", "18.9"], ["percent", "50 to 54 years", "11.5", "17", "12.1", "19.5"], ["percent", "55 to 59 years", "11.6", "21.9", "14.1", "20.8"], ["percent", "60 to 64 years", "16.7", "31.8", "14", "24.8"], ["percent", "65 to 69 years", "12.7", "48.8", "13.5", "39.8"], ["percent", "70 to 74 years", "15.3", "58.7", "11.1", "49.6"], ["percent", "75 to 79 years", "16.1", "59.7", "12.1", "58.6"], ["percent", "80 to 84 years", "20", "66.7", "9.3", "59.9"], ["percent", "85 years and older", "24.5", "67.4", "12.6", "58.8"]]}, "native_split": "test", "question": "how many percentage points was the low-income rate for women aged 85 years and older without transfers?", "source_prompt": "{table}\n{question}", "answer_dp": "67.4", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2543_13593_1", "source_table_id": "2543"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_609_3407_1", "source": "hitab", "table_key": "24e83c14df19c0ac79449587d1bbc3878f8cb01a", "grid": {"TD": 3, "LD": 2, "nrows": 24, "ncols": 6, "section_rows": [3, 7, 10, 14, 18, 21], "cells": [["", "", "level of education", "level of education", "level of education", "level of education"], ["", "", "less than a high school diploma", "high school diploma/some postsecondary", "trade certificate or college diploma", "university degree"], ["", "", "2014 dollars", "2014 dollars", "2014 dollars", "2014 dollars"], ["median employment income", "", "", "", "", ""], ["median employment income", "employed some time in reference year", "30400", "34500", "43700", "47100"], ["median employment income", "employed all year", "36200", "41200", "49800", "56200"], ["median employment income", "employed full year, full time in reference year", "36300", "43200", "51200", "58400"], ["median individual income", "", "", "", "", ""], ["median individual income", "total individual income before taxes", "26600", "34400", "44700", "46300"], ["median individual income", "total individual income after taxes", "24700", "30400", "38500", "40500"], ["median family income", "", "", "", "", ""], ["median family income", "total economic family income before taxes", "35200", "45200", "52200", "54000"], ["median family income", "total economic family income after taxes", "31700", "39500", "44900", "47200"], ["women", "", "", "", "", ""], ["employment income", "", "", "", "", ""], ["employment income", "employed some time in reference year", "13000", "20600", "28900", "37800"], ["employment income", "employed all year", "18400", "26400", "33700", "46300"], ["employment income", "employed full year, full time in reference year", "21100", "30200", "36000", "50000"], ["median individual income", "", "", "", "", ""], ["median individual income", "total individual income before taxes", "19100", "21700", "31400", "37600"], ["median individual income", "total individual income after taxes", "18800", "20800", "28900", "34200"], ["median family income", "", "", "", "", ""], ["median family income", "total economic family income before taxes", "25400", "36600", "46300", "56100"], ["median family income", "total economic family income after taxes", "24400", "32800", "40300", "48400"]]}, "native_split": "test", "question": "among women working full year and full time, what is the percentage change in the median employment income of high school graduates versus non-completers?", "source_prompt": "{table}\n{question}", "answer_dp": "0.43128", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_609_3407_1", "source_table_id": "609"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_280_totto31945-0_0_1", "source": "hitab", "table_key": "dd1b8c319100d47b32c27238e48677a3a3217625", "grid": {"TD": 2, "LD": 1, "nrows": 7, "ncols": 12, "section_rows": [], "cells": [["", "passing", "passing", "passing", "passing", "passing", "passing", "passing", "rushing", "rushing", "rushing", "rushing"], ["", "cmp", "att", "yds", "pct", "td", "int", "rtg", "att", "yds", "avg", "td"], ["2006", "31", "60", "291", "51.7", "1", "3", "87.9", "13", "37", "2.8", "0"], ["2007", "209", "382", "2431", "54.7", "12", "12", "112.3", "38", "27", "0.7", "0"], ["2008", "201", "343", "1960", "58.6", "8", "9", "109", "25", "22", "0.9", "0"], ["2009", "70", "131", "670", "53.4", "3", "5", "96.3", "17", "6", "0.4", "0"], ["ncaa career totals", "511", "916", "5352", "55.8", "24", "29", "107.2", "93", "38", "0.4", "0"]]}, "native_split": "test", "question": "how many passing yards did dan raudabaugh finish his career with?", "source_prompt": "{table}\n{question}", "answer_dp": "5352", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_280_totto31945-0_0_1", "source_table_id": "280_totto31945-0"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_200_1940_1", "source": "hitab", "table_key": "88fa0b69d1b8506b374f21dd91fadb88df7952d9", "grid": {"TD": 2, "LD": 3, "nrows": 22, "ncols": 9, "section_rows": [2, 7, 12, 17], "cells": [["", "", "", "first nations", "first nations", "inuit", "inuit", "non-indigenous comparison group", "non-indigenous comparison group"], ["", "", "", "non-employer", "employer", "non-employer", "employer", "non-employer", "employer"], ["percent", "goods", "", "", "", "", "", "", ""], ["percent", "goods", "born last 5 years", "30.6", "11.6", "78.9", "6.5", "19.4", "10.7"], ["percent", "goods", "born between 5 and 10 years", "21.7", "19.7", "16.3", "22.1", "46.3", "13.5"], ["percent", "goods", "born more than 10 years", "47.7", "68.6", "4.8", "71.5", "34.3", "75.7"], ["percent", "goods", "all age groups", "100", "100", "100", "100", "100", "100"], ["percent", "goods", "", "", "", "", "", "", ""], ["percent", "goods", "born last 5 years", "24.7", "9.1", "5.6", "", "21", "13"], ["percent", "goods", "born between 5 and 10 years", "24.9", "11.6", "7.5", "", "23.2", "13.2"], ["percent", "goods", "born more than 10 years", "50.4", "79.4", "86.9", "", "55.8", "73.8"], ["percent", "goods", "all age groups", "100", "100", "100", "100", "100", "100"], ["percent", "service", "", "", "", "", "", "", ""], ["percent", "service", "born last 5 years", "20.6", "27.5", "38.9", "26.9", "25.6", "14.3"], ["percent", "service", "born between 5 and 10 years", "24.9", "13.1", "22.2", "2.4", "33.7", "11.3"], ["percent", "service", "born more than 10 years", "54.5", "59.4", "38.9", "70.7", "40.7", "74.4"], ["percent", "service", "all age groups", "100", "100", "100", "100", "100", "100"], ["percent", "unassigned", "", "", "", "", "", "", ""], ["percent", "unassigned", "born last 5 years", "28.4", "37.7", "59.3", "", "44.4", "43.4"], ["percent", "unassigned", "born between 5 and 10 years", "54.8", "7.8", "25.3", "", "19.4", "22"], ["percent", "unassigned", "born more than 10 years", "16.8", "54.5", "15.5", "", "36.2", "34.6"], ["percent", "unassigned", "all age groups", "100", "100", "100", "100", "100", "100"]]}, "native_split": "test", "question": "what was the percentage of the share of revenues for employers in born more than 10 years for first nations csds in service?", "source_prompt": "{table}\n{question}", "answer_dp": "59.4", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_200_1940_1", "source_table_id": "200"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2417_12939_1", "source": "hitab", "table_key": "d8ecfde4d8767c762fad28b929104c3b9425f765", "grid": {"TD": 1, "LD": 2, "nrows": 35, "ncols": 17, "section_rows": [], "cells": [["", "", "n.l", "p.e.i", "n.s", "n.b", "que", "ont", "man", "sask", "alta", "b.c", "y.t", "n.w.t", "nvt", "unknown", "canada"], ["number", "1981", "98", "25", "139", "114", "807", "1073", "191", "203", "452", "424", "8", "28", "", "0", "3562"], ["number", "1986", "s", "13", "104", "81", "604", "969", "157", "157", "393", "355", "12", "28", "", "0", "2938"], ["number", "1991", "56", "13", "69", "58", "578", "953", "111", "126", "285", "298", "6", "20", "", "0", "2573"], ["number", "1996", "38", "8", "59", "40", "396", "802", "104", "112", "236", "237", "0", "19", "", "0", "2051"], ["number", "2001", "23", "10", "50", "31", "349", "713", "98", "68", "210", "168", "3", "3", "12", "1", "1739"], ["number", "2002", "21", "2", "36", "27", "346", "681", "98", "67", "283", "183", "3", "7", "8", "0", "1762"], ["number", "2003", "23", "7", "49", "29", "322", "692", "111", "76", "265", "170", "2", "4", "15", "0", "1765"], ["number", "2004", "23", "6", "40", "30", "342", "735", "97", "74", "236", "175", "4", "0", "12", "1", "1775"], ["number", "2005", "28", "3", "34", "28", "353", "745", "94", "99", "286", "183", "0", "3", "7", "0", "1863"], ["number", "2006", "24", "3", "34", "28", "415", "674", "88", "75", "238", "171", "3", "7", "10", "1", "1771"], ["number", "2007", "34", "7", "29", "31", "379", "723", "111", "77", "296", "176", "3", "3", "12", "0", "1881"], ["number", "2008", "25", "3", "32", "24", "379", "753", "101", "85", "317", "166", "2", "7", "13", "4", "1911"], ["number", "2009", "31", "5", "31", "43", "389", "705", "100", "96", "284", "161", "3", "11", "13", "0", "1872"], ["number", "2010", "26", "5", "41", "25", "440", "695", "106", "84", "299", "166", "2", "1", "12", "0", "1902"], ["number", "2011", "28", "6", "43", "25", "399", "664", "120", "97", "267", "171", "0", "5", "24", "0", "1849"], ["number", "2012", "25", "5", "40", "40", "441", "684", "92", "80", "223", "166", "1", "3", "18", "0", "1818"], ["number", "2013", "29", "3", "29", "33", "438", "681", "93", "111", "285", "161", "1", "5", "15", "0", "1884"], ["rate per 1,000", "1981", "10.8", "13.2", "11.5", "10.9", "8.5", "8.8", "11.9", "11.8", "10.6", "10.2", "14.9", "21.5", "", "", "9.6"], ["rate per 1,000", "1986", "8.5", "6.7", "8.4", "8.3", "7.1", "7.2", "9.2", "9", "9", "8.5", "24.8", "18.6", "", "", "7.9"], ["rate per 1,000", "1991", "7.8", "6.9", "5.7", "6.1", "5.9", "6.3", "6.4", "8.2", "6.7", "6.5", "10.6", "12.2", "", "", "6.4"], ["rate per 1,000", "1996", "6.6", "4.7", "5.6", "4.9", "4.6", "5.7", "6.7", "8.4", "6.2", "5.1", "0", "12.2", "", "", "5.6"], ["rate per 1,000", "2001", "4.9", "7.3", "5.6", "4.3", "4.7", "5.4", "7", "5.5", "5.6", "4.1", "8.7", "4.9", "16.9", "", "5.2"], ["rate per 1,000", "2002", "4.5", "1.5", "4.2", "3.8", "4.8", "5.3", "7.1", "5.7", "7.3", "4.6", "8.8", "11", "11", "", "5.4"], ["rate per 1,000", "2003", "5", "4.9", "5.7", "4.1", "4.4", "5.3", "8", "6.3", "6.6", "4.2", "6", "5.7", "19.8", "", "5.3"], ["rate per 1,000", "2004", "5.1", "4.3", "4.6", "4.3", "4.6", "5.5", "7", "6.2", "5.8", "4.3", "11", "0", "16.1", "", "5.3"], ["rate per 1,000", "2005", "6.2", "2.2", "4", "4.1", "4.6", "5.6", "6.6", "8.3", "6.8", "4.5", "0", "4.2", "10", "", "5.4"], ["rate per 1,000", "2006", "5.3", "2.1", "4", "4", "5.1", "5", "6", "6.1", "5.3", "4.1", "8.2", "10.2", "13.4", "", "5"], ["rate per 1,000", "2007", "7.5", "5", "3.3", "4.3", "4.5", "5.2", "7.3", "5.8", "6", "4", "8.5", "4.1", "15.1", "", "5.1"], ["rate per 1,000", "2008", "5.1", "2", "3.5", "3.2", "4.3", "5.4", "6.5", "6.2", "6.2", "3.8", "5.4", "9.7", "16.2", "", "5.1"], ["rate per 1,000", "2009", "6.3", "3.4", "3.4", "5.8", "4.4", "5", "6.3", "6.7", "5.5", "3.6", "7.8", "15.5", "14.8", "", "4.9"], ["rate per 1,000", "2010", "5.3", "3.6", "4.6", "3.4", "5", "5", "6.7", "5.9", "5.9", "3.8", "5.2", "1.4", "14.5", "", "5"], ["rate per 1,000", "2011", "6.3", "4.2", "4.9", "3.5", "4.5", "4.7", "7.7", "6.8", "5.2", "3.9", "0", "7.2", "28.7", "", "4.9"], ["rate per 1,000", "2012", "5.7", "3.8", "4.6", "5.7", "5", "4.8", "5.6", "5.4", "4.2", "3.8", "2.3", "4.4", "21.4", "", "4.8"], ["rate per 1,000", "2013", "6.4", "2.1", "3.4", "4.7", "4.9", "4.9", "5.6", "7.5", "5.3", "3.7", "2.5", "7.5", "16.4", "", "5"]]}, "native_split": "test", "question": "what was the infant mortality in british columbia in 2013?", "source_prompt": "{table}\n{question}", "answer_dp": "3.7", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2417_12939_1", "source_table_id": "2417"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_358_2004_3", "source": "hitab", "table_key": "eb04000e26e81449a11d75f3e285db4faf491213", "grid": {"TD": 3, "LD": 1, "nrows": 53, "ncols": 10, "section_rows": [], "cells": [["", "2016 population", "police officers", "police officers", "police officers", "police officers", "hires", "departures", "net gain or loss: hirings and departures", "police officers eligible to retire"], ["", "number", "2017 number of police officers", "2017 police officers per 100,000 population", "percentage change from previous year", "2017 percentage of female officers", "number", "number", "number", "percent"], ["", "", "number", "rate", "percent", "percent", "", "", "", ""], ["toronto, ont", "2876095", "5190", "180", "-4.9", "19.1", "26", "171", "-145", "7.1"], ["montreal, que", "2014221", "4596", "228", "-0.9", "32.1", "180", "220", "-40", "16.3"], ["peel region (mississauga/brampton), ont", "1404431", "1973", "140", "-2", "18.3", "100", "74", "26", "4.5"], ["calgary, alta", "1318817", 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b.c", "514522", "732", "142", "-1.3", "18.4", "72", "19", "53", "5.9"], ["niagara region (st. catharines), ont", "453817", "706", "156", "-0.8", "16.6", "22", "18", "4", "6.8"], ["laval, que", "429413", "611", "142", "0.9", "30", "11", "15", "-4", "12.3"], ["longueuil, que", "423525", "568", "134", "-0.4", "34.7", "10", "12", "-2", "3.5"], ["london, ont", "397493", "605", "152", "2.5", "20.3", "38", "29", "9", "3.6"], ["gatineau, que", "281392", "393", "140", "-1.7", "22.6", "13", "15", "-2", "4.3"], ["saskatoon, sask", "266064", "460", "173", "-1.4", "23", "17", "14", "3", "10.7"], ["burnaby, b.c", "246987", "290", "117", "1.5", "23.4", "31", "7", "24", "7.6"], ["halifax, n.s", "238467", "531", "223", "1.7", "19", "21", "17", "4", "2.3"], ["regina, sask", "223637", "397", "178", "-1.2", "24.9", "9", "11", "-2", "2.5"], ["windsor, ont", "221862", "428", "193", "-1.2", "15.9", "24", "17", "7", "4"], ["richmond, b.c", "209738", "206", "98", "1.3", "18.4", "30", "6", "24", "7.3"], ["st. john's, n.l", "207695", "341", "164", "-1.2", "28.7", "15", "16", "-1", "24"], ["richelieu saint-laurent, que", "205564", "212", "103", "-0.8", "23.1", "0", "12", "-12", "0"], ["greater sudbury, ont", "165154", "264", "160", "0", "17.4", "6", "9", "-3", "4.5"], ["sherbrooke, que", "164538", "200", "122", "-2.6", "23.5", "1", "4", "-3", "1.5"], ["barrie, ont", "150443", "239", "159", "-0.6", "15.9", "0", "2", "-2", "2.5"], ["saguenay, que", "147543", "178", "121", "0.2", "24.2", "0", "9", "-9", "3.4"], ["abbotsford, b.c", "146498", "203", "139", "-5.6", "18.2", "9", "8", "1", "3"], ["levis, que", "144918", "155", "107", "0.4", "21.3", "8", "7", "1", "10.3"], ["terrebonne, que", "139818", "169", "121", "0.7", "27.2", "19", "2", "17", "3"], ["coquitlam, b.c", "139055", "163", "117", "-3.9", "26.4", "11", "8", "3", "8.6"], ["trois-rivieres, que", "136168", "157", "115", "-0.5", "19.7", "4", "4", "0", "2.5"], ["guelph, ont", "132350", "194", "147", "-0.8", "22.7", "2", "9", "-7", "7.7"], ["kingston, ont", "131746", "203", "154", "-0.1", "18.7", "3", "4", "-1", "3.4"], ["kelowna, b.c", "129512", "173", "134", "-4.3", "31.8", "9", "10", "-1", "9.8"], ["codiac region (moncton), n.b", "118122", "134", "113", "-9.8", "18.7", "6", "14", "-8", "17.2"], ["saanich, b.c", "117285", "158", "135", "-1.2", "24.7", "7", "8", "-1", "5.1"], ["thunder bay, ont", "115740", "228", "197", "-1.3", "16.7", "6", "5", "1", "3.5"], ["langley township, b.c", "114940", "139", "121", "-1.6", "25.2", "13", "3", "10", "8.6"], ["delta, b.c", "110730", "168", "152", "6.6", "22.6", "14", "7", "7", "3"], ["roussillon region, que", "105835", "115", "109", "-1", "25.2", "0", "2", "-2", "1.7"], ["chatham-kent, ont", "104859", "155", "148", "-2.3", "13.5", "3", "2", "1", "10.3"], ["victoria, b.c", "104777", "244", "233", "-0.9", "25.4", "7", "6", "1", "6.1"], ["red deer, alta", "103690", "172", "166", "3.7", "22.7", "11", "6", "5", "5.8"], ["brantford, ont", "100791", "173", "172", "0.1", "15", "8", "4", "4", "0"]]}, "native_split": "test", "question": "how many did the rate of police strength in abbotsford, british columbia increase in 2016?", "source_prompt": "{table}\n{question}", "answer_dp": "-5.6", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_358_2004_3", "source_table_id": "358"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2775_15116_1(1)", "source": "hitab", "table_key": "0491868a70b49f1f010ff46652dee8de9d0e37f4", "grid": {"TD": 2, "LD": 2, "nrows": 16, "ncols": 8, "section_rows": [2], "cells": [["", "", "2012/2013", "2012/2013", "2013/2014", "2013/2014", "percent change in number of cases 2012/2013 to 2013/2014", "difference in median length of cases 2012/2013 to 2013/2014"], ["", "", "number", "median length of case", "number", "median length of case", "percent", "days"], ["canada", "", "387614", "120", "360640", "123", "-7", "3"], ["canada", "newfoundland and labrador", "5439", "113", "5150", "146", "-5", "33"], ["canada", "prince edward island", "1432", "35", "1312", "37", "-8", "2"], ["canada", "nova scotia", "11908", "158", "11485", "155", "-4", "-3"], ["canada", "new brunswick", "7784", "94", "7341", "104", "-6", "10"], ["canada", "quebec", "74051", "215", "62844", "238", "-15", "23"], ["canada", "ontario", "144399", "93", "133890", "99", "-7", "6"], ["canada", "manitoba", "19096", "160", "19058", "159", "0", "-1"], ["canada", "saskatchewan", "23781", "78", "23053", "71", "-3", "-7"], ["canada", "alberta", "55823", "121", "56346", "127", "1", "6"], ["canada", "british columbia", "39424", "120", "35850", "113", "-9", "-7"], ["canada", "yukon", "931", "98", "985", "92", "6", "-6"], ["canada", "northwest territories", "1578", "59", "1465", "63", "-7", "4"], ["canada", "nunavut", "1968", "55", "1861", "66", "-5", "12"]]}, "native_split": "test", "question": "what percentage of increase happened in manitoba?", "source_prompt": "{table}\n{question}", "answer_dp": "0", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2775_15116_1(1)", "source_table_id": "2775"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2767_15062_1", "source": "hitab", "table_key": "0f4a39e82aa971fada764bd9454cba8539f75749", "grid": {"TD": 1, "LD": 3, "nrows": 36, "ncols": 7, "section_rows": [1, 5, 9, 12, 16, 20, 23, 27, 30, 33], "cells": [["", "", "", "yukon", "northwest territories", "nunavut", "all territories"], ["percent", "location of the incident", "", "", "", "", ""], ["percent", "location of the incident", "private residence", "", "35", "29", "31"], ["percent", "location of the incident", "commercial or institutional establishment or public place", "67", "59", "70", "65"], ["percent", "location of the incident", "other", "", "", "", ""], ["percent", "location of the incident is the victim's place of work", "", "", "", "", ""], ["percent", "location of the incident is the victim's place of work", "yes", "", "", "", "20"], ["percent", "location of the incident is the victim's place of work", "no", "70", "78", "74", "74"], ["percent", "location of the incident is the victim's place of work", "don't know/refusal", "", "", "", ""], ["percent", "sex of offender", "", "", "", "", ""], ["percent", "sex of offender", "male", "68", "81", "80", "77"], ["percent", "sex of offender", "female", "", "", "", "23"], ["percent", "age of offender", "", "", "", "", ""], ["percent", "age of offender", "under 25 years old", "", "", "", "33"], ["percent", "age of offender", "25 to 44", "", "53", "47", "47"], ["percent", "age of offender", "45 years or older", "", "", "", "17"], ["percent", "relationship of offender to the victim", "", "", "", "", ""], ["percent", "relationship of offender to the victim", "stranger", "", "50", "", "41"], ["percent", "relationship of offender to the victim", "known to victim", "64", "50", "65", "59"], ["percent", "relationship of offender to the victim", "don't know/refusal", "", "", "", ""], ["percent", "number of offenders", "", "", "", "", ""], ["percent", "number of offenders", "one", "87", "84", "85", "85"], ["percent", "number of offenders", "two or more", "", "", "", ""], ["percent", "incident related to the offender's alcohol or drug use", "", "", "", "", ""], ["percent", "incident related to the offender's alcohol or drug use", "yes", "63", "72", "54", "63"], ["percent", "incident related to the offender's alcohol or drug use", "no", "", "", "", "26"], ["percent", "incident related to the offender's alcohol or drug use", "don't know/refusal", "", "", "", ""], ["percent", "presence of a weapon", "", "", "", "", ""], ["percent", "presence of a weapon", "yes", "", "", "", "27"], ["percent", "presence of a weapon", "no", "71", "67", "70", "69"], ["percent", "caused injuries", "", "", "", "", ""], ["percent", "caused injuries", "yes", "", "", "", "16"], ["percent", "caused injuries", "no", "93", "86", "73", "84"], ["percent", "victim reports effects consistent with post-traumatic stress disorder symptoms", "", "", "", "", ""], ["percent", "victim reports effects consistent with post-traumatic stress disorder symptoms", "yes", "", "", "", ""], ["percent", "victim reports effects consistent with post-traumatic stress disorder symptoms", "no", "91", "86", "86", "88"]]}, "native_split": "test", "question": "according to the victims, what percentage of criminals are under 45?", "source_prompt": "{table}\n{question}", "answer_dp": "80", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2767_15062_1", "source_table_id": "2767"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2769_15079_1", "source": "hitab", "table_key": "6f55297a5c68960ef800c684b4c78dbad97fd87b", "grid": {"TD": 1, "LD": 2, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["", "", "yukon", "northwest territories", "nunavut", "all territories"], ["percent", "crime was minor and not worth taking the time to report", "81", "82", "70", "78"], ["percent", "police wouldn't have considered the incident important enough", "74", "67", "62", "68"], ["percent", "incident was a private or personal matter and was handled informally", "48", "51", "51", "50"], ["percent", "no one was harmed/no financial loss", "53", "49", "46", "49"], ["percent", "lack of evidence", "45", "53", "42", "47"], ["percent", "police wouldn't have found property/offender", "46", "50", "41", "46"], ["percent", "did not want the hassle of dealing with the police", "38", "48", "40", "42"], ["percent", "police would not have been effective", "36", "38", "39", "37"], ["percent", "offender would not be convicted or adequately punished", "31", "42", "40", "38"], ["percent", "no harm was intended", "33", "37", "34", "35"], ["percent", "feared or did not want the hassle of dealing with the court process", "20", "33", "", "25"], ["percent", "did not want to get the offender in trouble", "", "", "", "15"], ["percent", "unsatisfactory service (from police) in the past", "15", "18", "10", "14"], ["percent", "didn't want others to know", "", "", "", "8"], ["percent", "reported to another official", "", "", "", "8"], ["percent", "police would be biased", "", "", "", "7"], ["percent", "insurance wouldn't cover it", "", "", "", "7"], ["percent", "nothing was taken/all items were recovered", "", "", "", "6"], ["percent", "did not need a police report to file claim", "", "", "", "4"]]}, "native_split": "test", "question": "what is the percentage of cases cited for not reporting an incident to the police in the territories were because the police would not consider the incident important enough?", "source_prompt": "{table}\n{question}", "answer_dp": "68", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2769_15079_1", "source_table_id": "2769"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1835_10372_1", "source": "hitab", "table_key": "8754441c3789d85c2f34a6f1f7a6fd896d582b63", "grid": {"TD": 2, "LD": 2, "nrows": 22, "ncols": 7, "section_rows": [2, 12], "cells": [["", "", "2000", "2007", "2011", "2017", "change 2000 to 2017"], ["", "", "percent", "percent", "percent", "percent", "percentage points"], ["men", "", "", "", "", "", ""], ["men", "new england", "87.6", "84.4", "77.8", "83.5", "-4.1"], ["men", "middle atlantic", "84.7", "83.4", "77.3", "79.7", "-5"], ["men", "east north central", "87.4", "83.7", "76.6", "81.1", "-6.3"], ["men", "west north central", "88.9", "86.3", "81.1", "85", "-3.9"], ["men", "south atlantic", "87.4", "85.4", "76.2", "81.6", "-5.9"], ["men", "south central", "85.8", "84.6", "78.9", "81.1", "-4.7"], ["men", "mountain", "88.9", "87.6", "77.7", "84.7", "-4.2"], ["men", "pacific", "85.9", "85.2", "76.6", "81.8", "-4.1"], ["men", "all regions", "86.7", "84.9", "77.5", "81.8", "-5"], ["women", "", "", "", "", "", ""], ["women", "new england", "76.4", "72.5", "69.3", "70.2", "-6.2"], ["women", "middle atlantic", "68.9", "68", "63.5", "65.5", "-3.4"], ["women", "east north central", "73.8", "70.4", "65", "67.7", "-6.2"], ["women", "west north central", "79.5", "77.1", "73.6", "74.4", "-5.1"], ["women", "south atlantic", "73.2", "70.5", "65.1", "66", "-7.3"], ["women", "south central", "68.6", "65.1", "60.7", "63.4", "-5.2"], ["women", "mountain", "70.8", "69.5", "63.7", "65.5", "-5.3"], ["women", "pacific", "67.6", "65", "60.5", "63.9", "-3.7"], ["women", "all regions", "71.4", "68.7", "63.9", "66", "-5.4"]]}, "native_split": "test", "question": "regardless of the region considered, when was the employment rate of women aged 25 to 54 without a bachelor's degree higher, in 2000 or in 2017?", "source_prompt": "{table}\n{question}", "answer_dp": "2000", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1835_10372_1", "source_table_id": "1835"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2207_12044_2", "source": "hitab", "table_key": "59b5fabc13bb4dc78d330a153597e5ed5a7cabe6", "grid": {"TD": 2, "LD": 2, "nrows": 17, "ncols": 8, "section_rows": [], "cells": [["", "", "custody", "custody", "community supervision", "community supervision", "total correctional supervision", "total correctional supervision"], ["", "", "female", "aboriginal", "female", "aboriginal", "female", "aboriginal"], ["percent", "newfoundland and labrador", "12", "25", "25", "23", "18", "24"], ["percent", "prince edward island", "14", "3", "25", "5", "20", "4"], ["percent", "nova scotia", "13", "10", "23", "7", "18", "8"], ["percent", "new brunswick", "15", "10", "22", "10", "17", "10"], ["percent", "quebec", "11", "5", "18", "7", "14", "6"], ["percent", "ontario", "13", "12", "19", "10", "15", "12"], ["percent", "manitoba", "20", "74", "27", "58", "22", "70"], ["percent", "saskatchewan", "18", "76", "24", "73", "21", "75"], ["percent", "alberta", "17", "42", "26", "27", "21", "36"], ["percent", "british columbia", "11", "32", "19", "27", "15", "30"], ["percent", "yukon", "12", "64", "19", "51", "17", "55"], ["percent", "northwest territories", "8", "88", "16", "86", "11", "87"], ["percent", "nunavut", "6", "100", "13", "100", "9", "100"], ["percent", "total-provinces and territories", "14", "30", "21", "25", "17", "28"], ["percent", "federal", "8", "27", "8", "26", "8", "27"]]}, "native_split": "test", "question": "how many percent did aboriginal adults account for admissions to community supervision among the provinces and territories in 2016/2017?", "source_prompt": "{table}\n{question}", "answer_dp": "25", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2207_12044_2", "source_table_id": "2207"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_50_164_tab3_489_2", "source": "hitab", "table_key": "0367b06043fc595d96348ded1b458c9cdc2342ea", "grid": {"TD": 3, "LD": 4, "nrows": 19, "ncols": 13, "section_rows": [3, 4, 7, 9, 16], "cells": [["", "", "", "", "total", "in labor force", "in labor force", "in labor force", "in labor force", "in labor force", "not in labor force", "not in labor force", "not in labor force"], ["", "", "", "", "", "all", "working for pay or profit", "working for pay or profit", "working for pay or profit", "unemployeda", "all", "retired", "not working, not seeking work"], ["", "", "", "", "", "", "all working", "full time", "part time", "", "", "", ""], ["all u.s. seh doctorate holders", "", "", "", "805500", "88.1", "86", "76.1", "9.9", "2.1", "11.9", "10", "1.9"], ["all u.s. seh doctorate holders", "sex", "", "", "", "", "", "", "", "", "", "", ""], ["all u.s. seh doctorate holders", "sex", "male", "", "551700", "87.7", "85.7", "77.9", "7.8", "2", "12.3", "11.5", "0.8"], ["all u.s. seh doctorate holders", "sex", "female", "", "253700", "88.9", "86.8", "72.4", "14.4", "2.1", "11.1", "6.7", "4.3"], ["all u.s. seh doctorate holders", "ethnicity and race", "", "", "", "", "", "", "", "", "", "", ""], ["all u.s. seh doctorate holders", "ethnicity and race", "hispanic or latino", "", "25800", "93", "91.1", "81.8", "8.9", "1.9", "7", "5", "1.9"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "", "779700", "87.9", "85.9", "76", "9.9", "2.1", "12.1", "10.2", "1.9"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "american indian or alaska native", "1700", "88.2", "88.2", "76.5", "11.8", "d", "5.9", "d", "d"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "asian", "147500", "93.6", "91.3", "86.4", "4.7", "2.3", "6.4", "4.7", "1.7"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "black or african american", "24500", "93.9", "91", "80.4", "11", "2.4", "6.1", "4.9", "1.2"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "native hawaiian or other pacific islander", "900", "88.9", "88.9", "77.8", "11.1", "d", "11.1", "d", "11.1"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "white", "596300", "86.2", "84.2", "73.1", "11.1", "2", "13.8", "11.8", "1.9"], ["all u.s. seh doctorate holders", "ethnicity and race", "not hispanic or latino", "more than one race", "8800", "93.2", "89.8", "79.5", "10.2", "2.3", "6.8", "4.5", "2.3"], ["all u.s. seh doctorate holders", "citizenship", "", "", "", "", "", "", "", "", "", "", ""], ["all u.s. seh doctorate holders", "citizenship", "u.s. citizen or permanent resident", "", "775300", "87.7", "85.6", "75.4", "10.2", "2.1", "12.3", "10.4", "1.9"], ["all u.s. seh doctorate holders", "citizenship", "temporary visa holder", "", "30200", "98.3", "97.4", "95.4", "2", "1", "1.7", "d", "1.3"]]}, "native_split": "test", "question": "in 2010, how many percent was the labor force participation rate among seh doctorate holders for men?", "source_prompt": "{table}\n{question}", "answer_dp": "87.7", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_50_164_tab3_489_2", "source_table_id": "50_164_tab3"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1493_8253_1", "source": "hitab", "table_key": "21ea83db1c2af7c6bae0ff37616c55abca54a787", "grid": {"TD": 2, "LD": 2, "nrows": 16, "ncols": 8, "section_rows": [2], "cells": [["", "", "2012/2013", "2012/2013", "2013/2014", "2013/2014", "percent change in number of cases 2012/2013 to 2013/2014", "difference in median length of cases 2012/2013 to 2013/2014"], ["", "", "number", "median length of case", "number", "median length of case", "percent", "days"], ["canada", "", "45441", "114", "39901", "120", "-12", "6"], ["canada", "newfoundland and labrador", "507", "104", "502", "120", "-1", "16"], ["canada", "prince edward island", "212", "22", "158", "50", "-25", "28"], ["canada", "nova scotia", "1328", "127", "1010", "113", "-24", "-14"], ["canada", "new brunswick", "880", "85", "755", "89", "-14", "4"], ["canada", "quebec", "8206", "136", "7646", "141", "-7", "5"], ["canada", "ontario", "17338", "112", "14617", "119", "-16", "7"], ["canada", "manitoba", "3311", "134", "3067", "146", "-7", "12"], ["canada", "saskatchewan", "4197", "78", "3755", "79", "-11", "1"], ["canada", "alberta", "6390", "127", "5701", "127", "-11", "0"], ["canada", "british columbia", "2682", "120", "2304", "117", "-14", "-3"], ["canada", "yukon", "64", "105", "75", "155", "17", "50"], ["canada", "northwest territories", "129", "78", "132", "81", "2", "3"], ["canada", "nunavut", "197", "64", "179", "71", "-9", "7"]]}, "native_split": "test", "question": "what was the changes in the number of completed cases in yukon from 2012/2013 to 2013/2014?", "source_prompt": "{table}\n{question}", "answer_dp": "17", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1493_8253_1", "source_table_id": "1493"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_2475_13218_1", "source": "hitab", "table_key": "45e8db136a41cc0d77e503bc052f7860e0de525a", "grid": {"TD": 3, "LD": 2, "nrows": 39, "ncols": 10, "section_rows": [4, 7, 12, 15, 19, 22, 26, 30, 33, 36], "cells": [["", "", "threshold gad", "threshold gad", "threshold gad", "threshold gad", "subthreshold gad", "subthreshold gad", "subthreshold gad", "subthreshold gad"], ["", "", "number '000", "%", "95% confidence interval", "95% confidence interval", "number '000", "%", "95% confidence interval", "95% confidence interval"], ["", "", "", "", "from", "to", "", "", "from", "to"], ["total", "", "721.7", "2.6", "2.3", "2.9", "654.7", "2.3", "2", "2.7"], ["sex", "", "", "", "", "", "", "", "", ""], ["sex", "men", "270.5", "2", "1.6", "2.4", "177.2", "1.3", "1", "1.6"], ["sex", "women", "451.2", "3.2", "2.8", "3.7", "477.6", "3.4", "2.8", "4"], ["age group", "", "", "", "", "", "", "", "", ""], ["age group", "15 to 24", "107.7", "2.4", "1.8", "3.2", "82.3", "1.9", "1.3", "2.7"], ["age group", "25 to 44", "268.2", "2.9", "2.4", "3.5", "229.1", "2.5", "2", "3.2"], ["age group", "45 to 64", "288.4", "3", "2.5", "3.6", "286.6", "3", "2.4", "3.7"], ["age group", "65 or older", "57.4", "1.2", "0.9", "1.7", "56.7", "1.2", "0.9", "1.6"], ["highest level of household education", "", "", "", "", "", "", "", "", ""], ["highest level of household education", "postsecondary completion", "498.5", "2.5", "2.2", "2.8", "443.3", "2.2", "1.9", "2.5"], ["highest level of household education", "less than postsecondary completion", "169.5", "3", "2.4", "3.7", "137.8", "2.4", "1.7", "3.4"], ["marital status", "", "", "", "", "", "", "", "", ""], ["marital status", "partner", "345.8", "2.1", "1.7", "2.5", "391.5", "2.3", "1.9", "10.2"], ["marital status", "widowed/separated/divorced", "137", "3.8", "3", "4.8", "80.3", "2.2", "1.8", "11.2"], ["marital status", "never married", "235.2", "3.1", "2.6", "3.7", "182.2", "2.4", "1.9", "11.2"], ["place of residence", "", "", "", "", "", "", "", "", ""], ["place of residence", "rural", "122", "2.5", "1.9", "3.2", "131.1", "2.7", "2", "3.6"], ["place of residence", "population centre", "599.7", "2.6", "2.3", "2.9", "523.6", "2.3", "1.9", "2.6"], ["mood disorder", "", "", "", "", "", "", "", "", ""], ["mood disorder", "past 12 months", "404.5", "27.2", "23.6", "31.2", "152.7", "10.3", "8", "13.1"], ["mood disorder", "lifetime, not past 12 months", "65.2", "3.3", "2.3", "4.7", "134.5", "6.8", "5.4", "8.5"], ["mood disorder", "none", "246.7", "1", "0.9", "1.2", "361.3", "1.5", "1.2", "1.8"], ["substance use disorder", "", "", "", "", "", "", "", "", ""], ["substance use disorder", "past 12 months", "95.8", "8", "6.1", "10.4", "51.1", "4.3", "2.6", "6.8"], ["substance use disorder", "lifetime, not past 12 months", "197.8", "4.3", "3.5", "5.2", "159.6", "3.4", "2.7", "4.4"], ["substance use disorder", "none", "406", "1.9", "1.6", "2.2", "437.7", "2", "1.7", "2.4"], ["schizophrenia", "", "", "", "", "", "", "", "", ""], ["schizophrenia", "yes", "21.7", "", "", "", "11", "10.8", "5.6", "19.9"], ["schizophrenia", "no", "700", "2.5", "2.2", "2.8", "638.5", "2.3", "2", "2.6"], ["psychosis", "", "", "", "", "", "", "", "", ""], ["psychosis", "yes", "36.7", "13.6", "9.2", "19.5", "59.2", "21.9", "14.4", "31.8"], ["psychosis", "no", "680.7", "2.5", "2.2", "2.8", "595", "2.1", "1.9", "2.5"], ["ptsd", "", "", "", "", "", "", "", "", ""], ["ptsd", "yes", "113", "24.3", "18.4", "31.4", "69.2", "14.9", "9.8", "21.9"], ["ptsd", "no", "605.2", "2.2", "2", "2.5", "585", "2.1", "1.8", "2.5"]]}, "native_split": "test", "question": "in 2012, how many percent of canadians aged 15 or older met all three criteria for past 12-month gad?", "source_prompt": "{table}\n{question}", "answer_dp": "2.6", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_2475_13218_1", "source_table_id": "2475"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1959_10930_1", "source": "hitab", "table_key": "c710362a60c989c4ca7de1b30b05e4d5f71a9b22", "grid": {"TD": 3, "LD": 1, "nrows": 17, "ncols": 13, "section_rows": [], "cells": [["", "male homicide victims", "male homicide victims", "male homicide victims", "male homicide victims", "female homicide victims", "female homicide victims", "female homicide victims", "female homicide victims", "total homicide victims", "total homicide victims", "total homicide victims", "total homicide victims"], ["", "aboriginal", "aboriginal", "non-aboriginal", "non-aboriginal", "aboriginal", "aboriginal", "non-aboriginal", "non-aboriginal", "aboriginal", "aboriginal", "non-aboriginal", "non-aboriginal"], ["", "number", "rate", "number", "rate", "number", "rate", "number", "rate", "number", "rate", "number", "rate"], ["newfoundland and labrador", "1", "5.08", "4", "1.69", "0", "0", "2", "0.82", "1", "2.54", "6", "1.25"], ["prince edward island", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["nova scotia", "1", "5.07", "8", "1.81", "0", "0", "2", "0.44", "1", "2.48", "10", "1.11"], ["new brunswick", "0", "0", "9", "2.52", "0", "0", "2", "0.55", "0", "0", "11", "1.52"], ["quebec", "4", "4.24", "45", "1.12", "0", "0", "16", "0.39", "4", "2.13", "61", "0.75"], ["ontario", "17", "8.93", "132", "1.98", "1", "0.5", "51", "0.74", "18", "4.61", "183", "1.35"], ["manitoba", "21", "18.51", "12", "2.26", "6", "5.12", "3", "0.56", "27", "11.71", "15", "1.41"], ["saskatchewan", "29", "31.03", "13", "2.78", "7", "7.2", "5", "1.09", "36", "18.88", "18", "1.94"], ["alberta", "21", "14.91", "66", "3.31", "10", "7.09", "19", "0.97", "31", "10.99", "85", "2.16"], ["british columbia", "15", "10.98", "50", "2.27", "3", "2.14", "19", "0.85", "18", "6.5", "69", "1.55"], ["yukon", "2", "46.57", "2", "12.88", "0", "0", "0", "0", "2", "22.35", "2", "6.68"], ["northwest territories", "2", "16.94", "0", "0", "1", "8.44", "0", "0", "3", "12.68", "0", "0"], ["nunavut", "0", "0", "0", "0", "1", "6.21", "0", "0", "1", "3.06", "0", "0"], ["canada", "113", "13.2", "341", "2", "29", "3.3", "119", "0.69", "142", "8.19", "460", "1.34"]]}, "native_split": "test", "question": "what was the multiple relationship between the rate of homicide for aboriginal people in 2016 and for non-aboriginal people?", "source_prompt": "{table}\n{question}", "answer_dp": "6.11194", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1959_10930_1", "source_table_id": "1959"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1510_8389_1", "source": "hitab", "table_key": "95528a53b8aa2decfbee9352d1e21edb623b0ef3", "grid": {"TD": 1, "LD": 2, "nrows": 18, "ncols": 5, "section_rows": [], "cells": [["", "", "women", "men", "total"], ["percent", "crime was minor and not worth taking the time to report", "71", "75", "71"], ["percent", "incident was a private or personal matter and it was handled informally", "68", "62", "67"], ["percent", "no one was harmed", "66", "42", "63"], ["percent", "did not want the hassle of dealing with the police", "47", "33", "45"], ["percent", "police would not have considered incident important enough", "43", "43", "43"], ["percent", "lack of evidence", "46", "18", "43"], ["percent", "offender would not be convicted or adequately punished", "41", "27", "40"], ["percent", "no harm was intended", "38", "49", "39"], ["percent", "feared or did not want the hassle of dealing with the court process", "34", "37", "34"], ["percent", "did not want the offender in trouble with the law", "29", "36", "30"], ["percent", "did not want others to find out about the victimization", "32", "", "30"], ["percent", "police would not have been efficient or effective", "27", "", "26"], ["percent", "fear of revenge by the offender or others", "24", "", "22"], ["percent", "police would not have been able to find or identify the offender", "22", "", "21"], ["percent", "police would be biased", "13", "", "13"], ["percent", "received unsatisfactory service from the police in the past", "13", "", "13"], ["percent", "afraid reporting would bring shame and dishonour to the family", "13", "", "12"]]}, "native_split": "test", "question": "what was the percentage of victims of sexual assault stated that they did not report the incident to the police because of a lack of evidence?", "source_prompt": "{table}\n{question}", "answer_dp": "43", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1510_8389_1", "source_table_id": "1510"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_449_2451_2", "source": "hitab", "table_key": "5ffcdd98bb34d0dae70da0f7bddde4680dcb077a", "grid": {"TD": 3, "LD": 2, "nrows": 32, "ncols": 10, "section_rows": [3, 9, 12, 15, 18, 23], "cells": [["", "", "2018", "2018", "2018", "2018", "2016", "2016", "2016", "2016"], ["", "", "male victims", "female victims", "total victims", "total victims", "male victims", "female victims", "total victims", "total victims"], ["", "", "percent", "percent", "number", "percent", "percent", "percent", "number", "percent"], ["environmental command", "", "", "", "", "", "", "", "", ""], ["environmental command", "canadian army", "1.2", "5.7", "360", "1.7", "1.3", "6", "380", "1.8"], ["environmental command", "royal canadian navy", "1.6", "6", "170", "2.2", "1.7", "7", "190", "2.4"], ["environmental command", "royal canadian air force", "1", "4.5", "190", "1.5", "0.8", "4.1", "160", "1.3"], ["environmental command", "military personnel command", "0.9", "2.4", "70", "1.4", "1.2", "4.4", "120", "2.3"], ["environmental command", "other", "0.8", "2.9", "110", "1.2", "0.9", "2.7", "100", "1.2"], ["indigenous identity", "", "", "", "", "", "", "", "", ""], ["indigenous identity", "indigenous (first nations, metis, inuit) person", "1.9", "7.7", "70", "3", "2", "", "50", "2.3"], ["indigenous identity", "non-indigenous person", "1.1", "4.1", "820", "1.5", "1.2", "4.9", "910", "1.7"], ["persons with disabilities", "", "", "", "", "", "", "", "", ""], ["persons with disabilities", "persons with disabilities", "2.4", "", "70", "3", "2.3", "", "50", "2.6"], ["persons with disabilities", "persons without disabilities", "1.1", "4.2", "830", "1.5", "1.1", "4.8", "900", "1.7"], ["visible minority", "", "", "", "", "", "", "", "", ""], ["visible minority", "visible minority", "1.8", "6.8", "100", "2.5", "2.3", "6", "90", "2.7"], ["visible minority", "non-visible minority", "1.1", "4.1", "790", "1.5", "1.1", "4.8", "870", "1.6"], ["rank", "", "", "", "", "", "", "", "", ""], ["rank", "junior non-commissioned member", "1.5", "5.4", "610", "2", "1.7", "6.2", "690", "2.3"], ["rank", "senior non-commissioned member", "0.7", "2.3", "120", "0.9", "0.5", "3.3", "120", "0.9"], ["rank", "junior officer", "1", "5.1", "120", "1.8", "1.1", "4.3", "120", "1.7"], ["rank", "senior officer", "0.4", "2.3", "40", "0.7", "", "2", "30", "0.5"], ["age group", "", "", "", "", "", "", "", "", ""], ["age group", "24 years and younger", "2.6", "15.4", "180", "3.9", "2.1", "10.2", "130", "2.9"], ["age group", "25 to 29 years", "1.6", "8", "250", "2.5", "2.1", "9.7", "320", "3"], ["age group", "30 to 34 years", "1", "3.7", "160", "1.4", "1.2", "4.9", "190", "1.7"], ["age group", "35 to 39 years", "0.9", "2.9", "130", "1.3", "0.9", "3.8", "130", "1.4"], ["age group", "40 to 44 years", "0.8", "2.8", "90", "1.2", "0.8", "2.8", "80", "1.1"], ["age group", "45 to 49 years", "0.7", "", "50", "0.8", "0.6", "2.7", "60", "0.9"], ["age group", "50 years and older", "0.4", "", "40", "0.6", "0.4", "2.3", "50", "0.7"], ["total", "", "1.1", "4.3", "900", "1.6", "1.2", "4.8", "960", "1.7"]]}, "native_split": "test", "question": "in 2018, what is the percentage of those 45 to 49 years of age having been sexually assaulted in the past 12 months?", "source_prompt": "{table}\n{question}", "answer_dp": "0.8", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_449_2451_2", "source_table_id": "449"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_55_totto6269-4_0_1", "source": "hitab", "table_key": "9d14460ac7b091347a2a98a6aba28d61f52e168e", "grid": {"TD": 2, "LD": 2, "nrows": 24, "ncols": 10, "section_rows": [], "cells": [["", "", "league", "league", "cup", "cup", "europe", "europe", "total", "total"], ["", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["sportul studentesc", "1998-99", "16", "2", "0", "0", "0", "0", "16", "2"], ["sportul studentesc", "1999-2000", "28", "6", "0", "0", "0", "0", "28", "6"], ["sportul studentesc", "2000-01", "27", "4", "5", "1", "0", "0", "32", "5"], ["sportul studentesc", "2001-02", "29", "12", "2", "1", "0", "0", "31", "13"], ["sportul studentesc", "2002-03", "12", "2", "0", "0", "0", "0", "12", "2"], ["sportul studentesc", "2003-04", "28", "29", "0", "0", "0", "0", "28", "29"], ["sportul studentesc", "2004-05", "29", "21", "2", "1", "0", "0", "29", "22"], ["total", "", "169", "76", "9", "3", "0", "0", "178", "79"], ["timisoara", "2005-06", "20", "2", "2", "1", "0", "0", "22", "3"], ["timisoara", "2006-07", "29", "9", "4", "2", "0", "0", "33", "11"], ["timisoara", "2007-08", "28", "16", "2", "1", "0", "0", "30", "17"], ["timisoara", "2008-09", "31", "17", "4", "2", "2", "1", "37", "20"], ["timisoara", "2009-10", "16", "8", "0", "0", "6", "3", "22", "11"], ["total", "", "124", "52", "12", "6", "8", "4", "144", "62"], ["kuban krasnodar", "2010", "31", "9", "0", "0", "0", "0", "31", "9"], ["kuban krasnodar", "2011-12", "39", "6", "1", "0", "0", "0", "40", "6"], ["kuban krasnodar", "2012-13", "21", "4", "2", "0", "0", "0", "23", "4"], ["kuban krasnodar", "2013-14", "28", "6", "0", "0", "6", "1", "34", "7"], ["kuban krasnodar", "2014-15", "24", "3", "3", "0", "0", "0", "27", "3"], ["kuban krasnodar", "2015-16", "15", "0", "1", "0", "0", "0", "16", "0"], ["total", "", "158", "28", "7", "0", "6", "1", "171", "29"], ["career total", "", "451", "156", "28", "9", "14", "5", "493", "170"]]}, "native_split": "test", "question": "how many goals did bucur got in sportul studentesc?", "source_prompt": "{table}\n{question}", "answer_dp": "76", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_55_totto6269-4_0_1", "source_table_id": "55_totto6269-4"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1902_10686_2", "source": "hitab", "table_key": "dd20fa5f854ff3122a9da978817ff7aa5f11983c", "grid": {"TD": 2, "LD": 3, "nrows": 7, "ncols": 8, "section_rows": [2], "cells": [["", "", "", "chronic conditions", "chronic conditions", "chronic conditions", "chronic conditions", "chronic conditions"], ["", "", "", "arthritis", "high blood pressure", "asthma", "mood disorder", "anxiety disorder"], ["percentage", "total aboriginal identity female population", "", "22.2", "17.2", "16.6", "16.9", "17"], ["percentage", "total aboriginal identity female population", "off-reserve first nations", "23.4", "17.4", "17.5", "18.6", "17.6"], ["percentage", "total aboriginal identity female population", "metis", "21.3", "17.1", "16.7", "16.2", "17.8"], ["percentage", "total aboriginal identity female population", "inuit", "15.2", "14.8", "9.3", "8.2", "7.1"], ["percentage", "non-aboriginal identity female population", "", "14.7", "13.1", "10.6", "10.3", "9.4"]]}, "native_split": "test", "question": "among metis women aged 15 and over, how many percent reported having an anxiety disorder?", "source_prompt": "{table}\n{question}", "answer_dp": "17.8", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1902_10686_2", "source_table_id": "1902"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_955_5498_2", "source": "hitab", "table_key": "d3a3ebe1589739baede256de2aa37fb840f8d4db", "grid": {"TD": 3, "LD": 1, "nrows": 19, "ncols": 13, "section_rows": [], "cells": [["", "custody", "custody", "custody", "custody", "custody", "community supervision", "community supervision", "community supervision", "community supervision", "community supervision", "total correctional services", "total correctional services"], ["", "sentenced custody", "remand", "other temporary custody", "total", "total percent change from 2017/2018", "probation", "conditional sentences", "other", "total", "total percent change from 2017/2018", "total", "total percent change from 2017/2018"], ["", "number", "number", "number", "number", "percent", "number", "number", "number", "number", "percent", "number", "percent"], ["newfoundland and labrador", "801", "838", "137", "1776", "-8", "1147", "344", "0", "1491", "-15", "3267", "-11"], ["prince edward island", "482", "284", "3", "769", "13", "835", "20", "179", "1034", "13", "1803", "13"], ["nova scotia", "1153", "3276", "266", "4695", "-2", "2368", "471", "0", "2839", "-36", "7534", "-18"], ["new brunswick", "2492", "2557", "699", "5748", "5", "1625", "488", "164", "2277", "4", "8025", "5"], ["quebec", "11933", "27800", "3361", "43094", "-5", "9744", "1603", "13285", "24632", "-7", "67726", "-6"], ["ontario", "18905", "41131", "4782", "64818", "-6", "25118", "3487", "745", "29350", "-5", "94168", "-6"], ["manitoba", "6605", "13925", "7611", "28141", "-6", "7354", "638", "1191", "9183", "-8", "37324", "-6"], ["saskatchewan", "4557", "8369", "234", "13160", "-1", "4686", "1465", "4128", "10279", "-3", "23439", "-2"], ["alberta", "15208", "20273", "2004", "37485", "-15", "8402", "926", "22477", "31805", "2", "69290", "-8"], ["british columbia", "9144", "12934", "1354", "23432", "-10", "8066", "1650", "17504", "27220", "-7", "50652", "-8"], ["yukon", "127", "297", "0", "424", "-11", "237", "79", "657", "973", "-7", "1397", "-8"], ["northwest territories", "361", "670", "0", "1031", "-4", "327", "44", "306", "677", "14", "1708", "2"], ["nunavut", "544", "913", "18", "1475", "50", "198", "45", "770", "1013", "13", "2488", "32"], ["total-provinces and territories", "72312", "133267", "20469", "226048", "-7", "70107", "11260", "61406", "142773", "-5", "368821", "-6"], ["federal", "5161", "", "2397", "7558", "3", "", "", "6990", "6990", "-2", "14548", "1"], ["total-all jurisdictions", "77473", "133267", "22866", "233606", "-7", "70107", "11260", "68396", "149763", "-5", "383369", "-6"]]}, "native_split": "test", "question": "what is the percentage of federal admissions increased in 2018/2019?", "source_prompt": "{table}\n{question}", "answer_dp": "1", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_955_5498_2", "source_table_id": "955"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_104_432_1", "source": "hitab", "table_key": "f72dbc321cee50ed0c9900770497059b6734b231", "grid": {"TD": 2, "LD": 2, "nrows": 7, "ncols": 12, "section_rows": [], "cells": [["", "", "agricultural region 7 manitoba", "agricultural region 7 manitoba", "agricultural region 8 manitoba", "agricultural region 8 manitoba", "agricultural region 9 manitoba", "agricultural region 9 manitoba", "agricultural region 8b saskatchewan", "agricultural region 8b saskatchewan", "agricultural region 7 alberta", "agricultural region 7 alberta"], ["", "", "french-language workers", "other workers", "french-language workers", "other workers", "french-language workers", "other workers", "french-language workers", "other workers", "french-language workers", "other workers"], ["percent", "did not work in 2010", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["percent", "employee", "46.7", "39.5", "41.4", "46.9", "74.6", "77", "35", "32.3", "20.8", "27.2"], ["percent", "unpaid family worker", "0", "5.9", "8.6", "4.9", "0", "2.5", "0", "7.8", "10.4", "9.4"], ["percent", "self-employed, incorporated", "20", "21.1", "32.8", "15.6", "12.7", "6.1", "25", "19", "28.6", "16.5"], ["percent", "self-employed, non-incorporated", "31.1", "33.4", "17.2", "32.4", "9.9", "14.4", "40", "41", "39", "46.9"]]}, "native_split": "test", "question": "in manitoba's agricultural region 9, what was the percentage of agricultural workers were employees?", "source_prompt": "{table}\n{question}", "answer_dp": "74.6", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_104_432_1", "source_table_id": "104"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_248_1358_1", "source": "hitab", "table_key": "39811ebc3c1b239f48b62aa20cd3f9e683c72de2", "grid": {"TD": 2, "LD": 1, "nrows": 10, "ncols": 8, "section_rows": [], "cells": [["", "number of syrian refugees", "number of refugees from other countries", "distribution of syrian refugees", "distribution of refugees from other countries", "proportion of syrian refugees in the total refugee population", "proportion of syrian refugees who were government-assisted refugees", "proportion of syrian refugees admitted in 2015"], ["", "number", "number", "percent", "percent", "percent", "percent", "percent"], ["atlantic provinces", "2670", "765", "10.7", "4.9", "77.7", "85.8", "6.6"], ["montreal census metropolitan area", "4265", "1195", "17", "7.7", "78.1", "5.4", "46.5"], ["rest of quebec", "1030", "1970", "4.1", "12.6", "34.3", "76.7", "5.3"], ["toronto census metropolitan area", "4035", "3285", "16.1", "21.1", "55.1", "30.9", "37.4"], ["rest of ontario", "6175", "2910", "24.6", "18.7", "68", "65.3", "27.5"], ["prairies", "4735", "4055", "18.9", "26", "53.9", "64.8", "24"], ["british columbia", "2125", "1410", "8.5", "9.1", "60.1", "73.6", "29.4"], ["canada", "25035", "15580", "100", "100", "61.6", "52.8", "28.7"]]}, "native_split": "test", "question": "what is the percentage of syrian refugess in the total refugee population of the atlantic provinces?", "source_prompt": "{table}\n{question}", "answer_dp": "77.7", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_248_1358_1", "source_table_id": "248"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_895_5142_1", "source": "hitab", "table_key": "c519b9f5f423ad5a622b2352bbe5bbc24f70bde7", "grid": {"TD": 3, "LD": 1, "nrows": 13, "ncols": 10, "section_rows": [], "cells": [["", "all", "all", "all", "men", "men", "men", "women", "women", "women"], ["", "2011", "2015", "change", "2011", "2015", "change", "2011", "2015", "change"], ["", "percent", "percent", "percentage point", "percent", "percent", "percentage point", "percent", "percent", "percentage point"], ["united states, united kingdom, ireland, australia, new zealand", "78.6", "84.3", "5.7", "77.4", "85.1", "7.7", "79.6", "83.6", "4"], ["caribbean, central/south america", "65.1", "72.5", "7.4", "65.4", "73", "7.6", "64.7", "72.2", "7.5"], ["western/northern europe", "79.3", "82.6", "3.3", "81.5", "84", "2.5", "77.1", "81.3", "4.2"], ["southern europe", "73.1", "72.5", "-0.6", "74.2", "73.6", "-0.6", "72.1", "71.4", "-0.7"], ["eastern europe", "66.7", "69.7", "3", "66.7", "70.6", "3.9", "66.7", "68.9", "2.2"], ["south asia", "72.4", "80.5", "8.1", "73.7", "79.5", "5.8", "71.1", "81.5", "10.4"], ["southeast asia", "64.5", "74.6", "10.1", "66.1", "74.1", "8", "63.3", "75", "11.7"], ["east asia", "59.8", "64.1", "4.3", "60.3", "65.4", "5.1", "59.3", "62.9", "3.6"], ["west central asia and middle east", "56.7", "73.2", "16.5", "56", "69.9", "13.9", "57.6", "76.5", "18.9"], ["africa", "67.1", "78.4", "11.3", "72.7", "79.4", "6.7", "60.4", "77.3", "16.9"]]}, "native_split": "test", "question": "between 2011 and 2015, what was the percentage points of the voting rate of immigrants from west central asia and the middle east increased?", "source_prompt": "{table}\n{question}", "answer_dp": "16.5", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_895_5142_1", "source_table_id": "895"}, "dataset": "hitab", "subtype": "number"}
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"lajos ludinszky", "o", "x--", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "1.6"], ["28", "michel meerz", "o", "x--", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "1.6"], ["28", "alfredo pagani", "o", "xxx", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "1.6"], ["34", "marius delaby", "--", "x--", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "none"], ["34", "armand estang", "--", "x--", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "none"], ["34", "john nicholson", "--", "xxx", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "none"], ["34", "angelo tonini", "x--", "xxx", "--", "--", "--", "", "", "", "", "", "", "", "", "", "", "", "none"]]}, "native_split": "test", "question": "how many metres did hans liesche set an olympic record with?", "source_prompt": "{table}\n{question}", "answer_dp": "1.91", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_370_totto42695-4_0_1", "source_table_id": "370_totto42695-4"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_36_totto4159-4_0_1", "source": "hitab", "table_key": "cbd0fb839dd053ad399e17345b8e164d8c06bdbb", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 12, "section_rows": [], "cells": [["", "", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions", "peak chart positions"], ["", "", "aus", "bel", "fra", "ger", "swi", "uk", "uk dance", "us", "us dance", "us indie"], ["fancypants hoodlum", "released: 1995 label: accudub inc. format: cd", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"], ["the teaches of peaches", "released: september 5, 2000 label: kitty-yo formats: cd, lp, digital download", "-", "-", "-", "-", "-", "-", "-", "-", "-", "-"], ["fatherfucker", "released: september 23, 2003 label: xl formats: cd, lp, digital download", "-", "-", "132", "-", "-", "93", "3", "-", "5", "33"], ["impeach my bush", "released: july 11, 2006 label: xl formats: cd, lp, digital download", "70", "-", "156", "-", "-", "132", "3", "168", "5", "13"], ["i feel cream", "released: may 1, 2009 label: xl formats: cd, lp, digital download", "65", "73", "184", "75", "75", "152", "-", "160", "5", "23"], ["rub", "released: september 25, 2015 label: i u she music formats: cd, lp, digital download", "-", "110", "-", "-", "-", "187", "-", "-", "4", "38"], ["\"-\" denotes a recording that did not chart or was not released in that territory", "", "", "", "", "", "", "", "", "", "", ""]]}, "native_split": "test", "question": "in 2006, what is the ranking for peaches released song \"impeach my bush,\" in the us.", "source_prompt": "{table}\n{question}", "answer_dp": "168", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_36_totto4159-4_0_1", "source_table_id": "36_totto4159-4"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_65_20_nsf20316-tab001_59_2", "source": "hitab", "table_key": "1e6bb369e752adfd406c882b7a77390a4f6ca356", "grid": {"TD": 1, "LD": 4, "nrows": 31, "ncols": 6, "section_rows": [1, 2, 6, 10, 14, 17, 18, 21, 24], "cells": [["", "", "", "", "2017", "2018"], ["domestic r&d performancea", "", "", "", "400100", "441036"], ["domestic r&d performancea", "type of r&db", "", "", "", ""], ["domestic r&d performancea", "type of r&db", "basic research", "", "24829", "28980"], ["domestic r&d performancea", "type of r&db", "applied research", "", "62132", "65222"], ["domestic r&d performancea", "type of r&db", "development", "", "313139", "346834"], ["domestic r&d performancea", "paid for by the companyc", "", "", "339036", "377806"], ["domestic r&d performancea", "paid for by the companyc", "basic research", "", "18732", "22312"], ["domestic r&d performancea", "paid for by the companyc", "applied research", "", "49149", "53229"], ["domestic r&d performancea", "paid for by the companyc", "development", "", "271155", "302264"], ["domestic r&d performancea", "paid for by others", "", "", "61065", "63230"], ["domestic r&d performancea", "paid for by others", "basic research", "", "6097", "6668"], ["domestic r&d performancea", "paid for by others", "applied research", "", "12984", "11993"], ["domestic r&d performancea", "paid for by others", "development", "", "41984", "44570"], ["domestic r&d performancea", "paid for by others", "source of funds", "", "", ""], ["domestic r&d performancea", "paid for by others", "source of funds", "federal", "24277", "24685"], ["domestic r&d performancea", "paid for by others", "source of funds", "otherd", "36788", "38545"], ["size of company", "", "", "", "", ""], ["size of company", "small companiese", "", "", "", ""], ["size of company", "small companiese", "10-19", "", "3311", "4390"], ["size of company", "small companiese", "20-49", "", "9435", "11252"], ["size of company", "medium companies", "", "", "", ""], ["size of company", "medium companies", "50-99", "", "10141", "12321"], ["size of company", "medium companies", "100-249", "", "17216", "18547"], ["size of company", "large companies", "", "", "", ""], ["size of company", "large companies", "250-499", "", "14103", "19645"], ["size of company", "large companies", "500-999", "", "17871", "17657"], ["size of company", "large companies", "1,000-4,999", "", "65112", "68578"], ["size of company", "large companies", "5,000-9,999", "", "40198", "45337"], ["size of company", "large companies", "10,000-24,999", "", "73485", "84420"], ["size of company", "large companies", "25,000 or more", "", "149227", "158889"]]}, "native_split": "test", "question": "funding from the companies' own sources was $378 billion in 2018, how many percent of increase from 2017?", "source_prompt": "{table}\n{question}", "answer_dp": "0.114354", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_65_20_nsf20316-tab001_59_2", "source_table_id": "65_20_nsf20316-tab001"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_751_4241_1", "source": "hitab", "table_key": "dd3f762ea2128fb28891af327944ead6b5e32f19", "grid": {"TD": 1, "LD": 3, "nrows": 25, "ncols": 7, "section_rows": [1, 7, 13, 19, 20], "cells": [["", "", "", "as a percent of visible minority females in each cma", "as a percent of total female population in each cma/municipality", "largest visible minority group in each cma/municipality", "largest visible minority group as a percent of total female population in each cma/municipality"], ["toronto cma", "", "", "100", "47.6", "south asian", "14.7"], ["toronto cma", "toronto", "", "49.3", "49.8", "south asian", "11.8"], ["toronto cma", "mississauga", "", "14.5", "54.1", "south asian", "21.4"], ["toronto cma", "brampton", "", "13", "66.5", "south asian", "37.7"], ["toronto cma", "markham", "", "8.2", "72.5", "chinese", "38.5"], ["toronto cma", "richmond hill", "", "3.7", "53.4", "chinese", "23.6"], ["vancouver cma", "", "", "100", "46.1", "chinese", "18.6"], ["vancouver cma", "vancouver", "", "30.2", "53.7", "chinese", "28.6"], ["vancouver cma", "surrey", "", "22.9", "52.7", "south asian", "30.3"], ["vancouver cma", "richmond", "", "13.1", "71.3", "chinese", "47.7"], ["vancouver cma", "burnaby", "", "12.7", "60.9", "chinese", "31.6"], ["vancouver cma", "coquitlam", "", "5.3", "44.6", "chinese", "17.5"], ["montreal cma", "", "", "100", "20.2", "black", "5.9"], ["montreal cma", "montreal", "", "66.8", "31.3", "black", "9.4"], ["montreal cma", "laval", "", "10.6", "20.5", "black", "6.2"], ["montreal cma", "longueuil", "", "4.2", "14", "black", "4.8"], ["montreal cma", "brossard", "", "4", "38", "chinese", "12.3"], ["montreal cma", "dollard-des ormeaux", "", "2.4", "36.4", "south asian", "9.9"], ["calgary cma", "", "", "100", "28.4", "south asian", "6.9"], ["calgary cma", "calgary", "", "96.5", "30.3", "south asian", "7.3"], ["calgary cma", "calgary", "chestermere", "1.3", "29.3", "south asian", "9.5"], ["calgary cma", "calgary", "airdrie", "1.1", "8.6", "filipino", "2.1"], ["calgary cma", "calgary", "rocky view county", "1", "9.3", "south asian", "3.7"], ["calgary cma", "calgary", "cochrane", "0.2", "3.4", "filipino", "1.3"]]}, "native_split": "test", "question": "chinese women and girls made up the largest female visible minority group in richmond hill, what was the percentage of its total female population?", "source_prompt": "{table}\n{question}", "answer_dp": "23.6", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_751_4241_1", "source_table_id": "751"}, "dataset": "hitab", "subtype": "number"}
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{"id": "hitab:hitab_766_4368_1", "source": "hitab", "table_key": "d32b0e6dc9e2ed007ea7124d84b34e526d45069e", "grid": {"TD": 2, "LD": 3, "nrows": 7, "ncols": 7, "section_rows": [2], "cells": [["", "", "", "women", "women", "men", "men"], ["", "", "", "visible minority", "not a visible minority", "visible minority", "not a visible minority"], ["percentage", "total age groups", "", "27.7", "29.5", "16.8", "15.2"], ["percentage", "total age groups", "15 to 24", "66.2", "61.1", "57.2", "46.5"], ["percentage", "total age groups", "25 to 54", "20.1", "20.3", "9.2", "6.2"], ["percentage", "total age groups", "55 to 64", "22.7", "29.7", "11.3", "12.9"], ["percentage", "total age groups", "65 and over", "46.9", "56.7", "32.5", "39.4"]]}, "native_split": "test", "question": "what was the percentage of visible minority men worked mostly or entirely part time in 2010?", "source_prompt": "{table}\n{question}", "answer_dp": "16.8", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_766_4368_1", "source_table_id": "766"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_530_totto60719-1_0_1", "source": "hitab", "table_key": "cc9080b2ee18f152b54ac215a5227bb135d85bb8", "grid": {"TD": 2, "LD": 2, "nrows": 6, "ncols": 14, "section_rows": [], "cells": [["", "", "passing", "passing", "passing", "passing", "passing", "passing", "passing", "passing", "rushing", "rushing", "rushing", "rushing"], ["", "", "cmp", "att", "pct", "yds", "y/a", "td", "int", "rtg", "att", "yds", "avg", "td"], ["2007", "cincinnati", "11", "20", "55", "91", "4.6", "1", "3", "79.7", "7", "75", "10.7", "0"], ["2008", "cincinnati", "199", "324", "61.4", "2407", "7.4", "19", "11", "136.4", "56", "47", "0.8", "1"], ["2009", "cincinnati", "211", "338", "62.4", "2520", "7.5", "29", "6", "149.8", "31", "6", "0.2", "2"], ["career", "", "421", "682", "61.7", "5018", "7.4", "49", "20", "141.4", "94", "128", "1.4", "3"]]}, "native_split": "test", "question": "how many yards did pike throw for in 2009?", "source_prompt": "{table}\n{question}", "answer_dp": "2520", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_530_totto60719-1_0_1", "source_table_id": "530_totto60719-1"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_77_totto8964-3_0_1", "source": "hitab", "table_key": "b1ecec08f33529da470b82b9c39068aad8eb7bd1", "grid": {"TD": 2, "LD": 3, "nrows": 14, "ncols": 11, "section_rows": [], "cells": [["", "", "", "population", "population", "population", "population", "population", "population", "population", "population"], ["", "", "", "c. 1801", "1841", "1891", "1931", "1961", "1981", "2001", "2011"], ["1", "lewis and harris", "na h-eileanan siar", "12164", "20046", "30726", "28042", "24107", "22476", "19918", "21031"], ["2", "mainland, shetland", "shetland", "none", "20572", "19741", "15172", "13282", "17722", "17550", "18765"], ["3", "mainland, orkney", "orkney", "none", "16022", "16498", "13352", "13495", "14000", "15315", "17162"], ["4", "skye", "highland", "14,470", "23082", "15705", "9908", "7479", "7276", "9232", "10008"], ["5", "bute", "argyll and bute", "4,759", "7147", "11735", "12112", "9793", "7306", "7228", "6498"], ["6", "arran", "north ayrshire", "5,804", "6241", "4730", "4506", "3700", "3845", "5045", "4629"], ["7", "islay", "argyll and bute", "9,500", "15772", "7375", "4970", "3860", "3792", "3457", "3228"], ["8", "mull", "argyll and bute", "8,016", "8316", "4691", "2903", "2154", "2197", "2667", "2800"], ["9", "south uist", "na h-eileanan siar", "none", "5093", "3708", "2810", "2376", "2231", "1818", "1754"], ["10", "great cumbrae", "north ayrshire", "509", "1413", "1784", "2144", "1638", "1300", "1434", "1376"], ["none", "total", "none", "none", "123704", "116693", "95919", "81884", "82145", "83664", "87251"], ["none", "change", "none", "none", "none", "5.7", "7.8", "14.6", "0.3", "1.8", "4.3"]]}, "native_split": "test", "question": "list the rank of mainland island.", "source_prompt": "{table}\n{question}", "answer_dp": "2", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_77_totto8964-3_0_1", "source_table_id": "77_totto8964-3"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_491_2679_2", "source": "hitab", "table_key": "535289414e454905de53b18849b9ff49409e8c21", "grid": {"TD": 2, "LD": 2, "nrows": 21, "ncols": 5, "section_rows": [2, 5, 8, 11, 14, 17], "cells": [["", "", "%", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to"], ["discomfort or injury involving a laser product", "", "", "", ""], ["discomfort or injury involving a laser product", "yes", "1.1", "0.7", "1.4"], ["discomfort or injury involving a laser product", "no", "98.9", "98.5", "99.2"], ["discomfort or injury involving laser product-number of times", "", "", "", ""], ["discomfort or injury involving laser product-number of times", "1 time", "44.9", "27.6", "62.1"], ["discomfort or injury involving laser product-number of times", "2 to 260 times", "49.5", "32.2", "66.7"], ["type of discomfort or injury", "", "", "", ""], ["type of discomfort or injury", "skin", "41.3", "24.1", "58.5"], ["type of discomfort or injury", "eye", "59.1", "41.9", "76.3"], ["length of discomfort or injury", "", "", "", ""], ["length of discomfort or injury", "two days or less", "63.9", "45.2", "82.6"], ["length of discomfort or injury", "more than two days", "34", "15.2", "52.9"], ["own use or someone else's", "", "", "", ""], ["own use or someone else's", "your own use of the device", "25", "12.1", "37.8"], ["own use or someone else's", "someone else's use of the device", "74.9", "62", "87.7"], ["type of laser that caused injury, excluding medical devices", "", "", "", ""], ["type of laser that caused injury, excluding medical devices", "pointer", "26.3", "11.7", "40.9"], ["type of laser that caused injury, excluding medical devices", "cosmetic treatments", "39.1", "21.7", "56.6"], ["type of laser that caused injury, excluding medical devices", "survey tool / entertainment / materials processing / scanner / other", "34.1", "18.4", "49.8"]]}, "native_split": "test", "question": "what percentage of respondents reported that their injury was caused by their own personal use of a laser product?", "source_prompt": "{table}\n{question}", "answer_dp": "25", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_491_2679_2", "source_table_id": "491"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_112_489_2", "source": "hitab", "table_key": "6ea27e60c3cc43dbf0a38160b8253f6f89461440", "grid": {"TD": 2, "LD": 2, "nrows": 10, "ncols": 9, "section_rows": [2], "cells": [["", "", "area of agricultural land", "cropland", "cropland", "natural land for pasture", "natural land for pasture", "woodlands and wetlands", "woodlands and wetlands"], ["", "", "hectares", "hectares", "percent", "hectares", "percent", "hectares", "percent"], ["canada", "", "64812723", "35350270", "54.5", "14703330", "22.7", "4897367", "7.6"], ["canada", "atlantic provinces", "1063343", "430363", "40.5", "65711", "6.2", "450031", "42.3"], ["canada", "quebec", "3341333", "1874760", "56.1", "134147", "4", "1057417", "31.6"], ["canada", "ontario", "5126653", "3613821", "70.5", "398538", "7.8", "646578", "12.6"], ["canada", "manitoba", "7293839", "4348869", "59.6", "1466968", "20.1", "549444", "7.5"], ["canada", "saskatchewan", "24940023", "14728934", "59.1", "4816782", "19.3", "1009381", "4"], ["canada", "alberta", "20436150", "9753849", "47.7", "6435825", "31.5", "893436", "4.4"], ["canada", "british columbia", "2611382", "599674", "23", "1385359", "53.1", "291079", "11.1"]]}, "native_split": "test", "question": "how many hectares did saskatchewan report the area of woodland and wetland in 2011?", "source_prompt": "{table}\n{question}", "answer_dp": "1009381", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_112_489_2", "source_table_id": "112"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1569_8802_1", "source": "hitab", "table_key": "461d2e5c59e54115855d6b8358f486b26b6532c7", "grid": {"TD": 2, "LD": 3, "nrows": 7, "ncols": 7, "section_rows": [2], "cells": [["", "", "", "women", "women", "men", "men"], ["", "", "", "non-aboriginal", "aboriginal", "non-aboriginal", "aboriginal"], ["percent", "15 or older", "", "14.7", "22", "12.5", "14.6"], ["percent", "15 or older", "15 to 24", "4.1", "8.1", "4.4", "7"], ["percent", "15 or older", "25 to 54", "9.1", "21.3", "8.1", "12.1"], ["percent", "15 or older", "55 to 64", "20.8", "33.9", "19", "26.8"], ["percent", "15 or older", "65 or older", "35", "53.4", "30.6", "39.2"]]}, "native_split": "test", "question": "how many times aboriginal men aged 15 to 24 were more likely than non-aboriginal men to report disabilities?", "source_prompt": "{table}\n{question}", "answer_dp": "1.590909", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1569_8802_1", "source_table_id": "1569"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_933_5372_3", "source": "hitab", "table_key": "a4400a1a46960b63a8e5b6f171f5c46c505f5263", "grid": {"TD": 2, "LD": 3, "nrows": 26, "ncols": 13, "section_rows": [3, 11, 19], "cells": [["", "", "", "guilty", "guilty", "stayed/withdrawn", "stayed/withdrawn", "acquitted", "acquitted", "other", "other", "total", "total"], ["", "", "", "number", "percent", "number", "percent", "number", "percent", "number", "percent", "number", "percent"], ["completed cases related to drug possession", "cannabis", "", "25219", "43", "33369", "57", "92", "0", "258", "0", "58938", "100"], ["completed cases related to drug possession", "other drugs", "", "22740", "65", "12158", "35", "129", "0", "120", "0", "35147", "100"], ["completed cases related to drug possession", "other drugs", "cocaine", "13870", "66", "6933", "33", "88", "0", "63", "0", "20954", "100"], ["completed cases related to drug possession", "other drugs", "heroin", "743", "67", "355", "32", "4", "0", "1", "0", "1103", "100"], ["completed cases related to drug possession", "other drugs", "methamphetamines", "1223", "65", "641", "34", "7", "0", "8", "0", "1879", "100"], ["completed cases related to drug possession", "other drugs", "ecstasy", "510", "59", "345", "40", "1", "0", "8", "1", "864", "100"], ["completed cases related to drug possession", "other drugs", "other cdsa", "6394", "62", "3884", "38", "29", "0", "40", "0", "10347", "100"], ["completed cases related to drug possession", "total completed cases related to drug possession", "", "47959", "51", "45527", "48", "221", "0", "378", "0", "94085", "100"], ["completed cases related to drug supply", "cannabis", "", "7752", "49", "7883", "50", "148", "1", "85", "1", "15868", "100"], ["completed cases related to drug supply", "other drugs", "", "15248", "56", "11600", "43", "270", "1", "111", "0", "27229", "100"], ["completed cases related to drug supply", "other drugs", "cocaine", "10968", "58", "7774", "41", "206", "1", "77", "0", "19025", "100"], ["completed cases related to drug supply", "other drugs", "heroin", "740", "58", "504", "40", "22", "2", "3", "0", "1269", "100"], ["completed cases related to drug supply", "other drugs", "methamphetamines", "371", "52", "329", "46", "5", "1", "6", "1", "711", "100"], ["completed cases related to drug supply", "other drugs", "ecstasy", "256", "52", "230", "46", "4", "1", "6", "1", "496", "100"], ["completed cases related to drug supply", "other drugs", "other cdsa", "2913", "51", "2763", "48", "33", "1", "19", "0", "5728", "100"], ["completed cases related to drug supply", "total completed cases related to drug supply", "", "23000", "53", "19483", "45", "418", "1", "196", "0", "43097", "100"], ["completed drug-related cases", "cannabis", "", "32971", "44", "41252", "55", "240", "0", "343", "0", "74806", "100"], ["completed drug-related cases", "other drugs", "", "37988", "61", "23758", "38", "399", "1", "231", "0", "62376", "100"], ["completed drug-related cases", "other drugs", "cocaine", "24838", "62", "14707", "37", "294", "1", "140", "0", "39979", "100"], ["completed drug-related cases", "other drugs", "heroin", "1483", "63", "859", "36", "26", "1", "4", "0", "2372", "100"], ["completed drug-related cases", "other drugs", "methamphetamines", "1594", "62", "970", "37", "12", "0", "14", "1", "2590", "100"], ["completed drug-related cases", "other drugs", "ecstasy", "766", "56", "575", "42", "5", "0", "14", "1", "1360", "100"], ["completed drug-related cases", "other drugs", "other cdsa", "9307", "58", "6647", "41", "62", "0", "59", "0", "16075", "100"], ["completed drug-related cases", "total completed drug-related cases", "", "70959", "52", "65010", "47", "639", "0", "574", "0", "137182", "100"]]}, "native_split": "test", "question": "broken down by type of drug, what is the percentage of completed cases involving drugs other for supply with a guilty decision from 2008/2009 to 2011/2012?", "source_prompt": "{table}\n{question}", "answer_dp": "56", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_933_5372_3", "source_table_id": "933"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1771_10009_2", "source": "hitab", "table_key": "1afb1e33ee619b902a346c52c540208a23e747c2", "grid": {"TD": 1, "LD": 2, "nrows": 19, "ncols": 4, "section_rows": [1, 5, 12], "cells": [["", "", "number", "percentage points"], ["devitalization factors", "", "", ""], ["devitalization factors", "non-transmission of mother tongue", "-7075", "-23.4"], ["devitalization factors", "complete language transfers", "-630", "-2.1"], ["devitalization factors", "total losses", "-7705", "-25.5"], ["mitigating factors", "", "", ""], ["mitigating factors", "knowledge of mother tongue without using it at home", "475", "1.6"], ["mitigating factors", "use of inuktut at home among inuit who do not report it as mother tongue", "3445", "11.4"], ["mitigating factors", "main use", "525", "1.7"], ["mitigating factors", "secondary use", "2920", "9.7"], ["mitigating factors", "secondary speakers", "450", "1.5"], ["mitigating factors", "total mitigating factors", "4370", "14.5"], ["mitigating factors", "", "", ""], ["mitigating factors", "transmission of inuit mother tongue to non-inuit", "105", "0.3"], ["mitigating factors", "use of inuktut at home among non-inuit who do not report it as mother tongue", "225", "0.7"], ["mitigating factors", "secondary speakers", "130", "0.4"], ["mitigating factors", "total mitigating factors", "460", "1.5"], ["mitigating factors", "sum of mitigating factors", "4830", "16"], ["mitigating factors", "sum of devitalization factors and mitigating factors", "-2875", "-9.5"]]}, "native_split": "test", "question": "of the 630 inuit who have made a complete language transfer, how many inuit can still conduct a conversation in inuktut?", "source_prompt": "{table}\n{question}", "answer_dp": "475", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1771_10009_2", "source_table_id": "1771"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_933_5372_1", "source": "hitab", "table_key": "a4400a1a46960b63a8e5b6f171f5c46c505f5263", "grid": {"TD": 2, "LD": 3, "nrows": 26, "ncols": 13, "section_rows": [3, 11, 19], "cells": [["", "", "", "guilty", "guilty", "stayed/withdrawn", "stayed/withdrawn", "acquitted", "acquitted", "other", "other", "total", "total"], ["", "", "", "number", "percent", "number", "percent", "number", "percent", "number", "percent", "number", "percent"], ["completed cases related to drug possession", "cannabis", "", "25219", "43", "33369", "57", "92", "0", "258", "0", "58938", "100"], ["completed cases related to drug possession", "other drugs", "", "22740", "65", "12158", "35", "129", "0", "120", "0", "35147", "100"], ["completed cases related to drug possession", "other drugs", "cocaine", "13870", "66", "6933", "33", "88", "0", "63", "0", "20954", "100"], ["completed cases related to drug possession", "other drugs", "heroin", "743", "67", "355", "32", "4", "0", "1", "0", "1103", "100"], ["completed cases related to drug possession", "other drugs", "methamphetamines", "1223", "65", "641", "34", "7", "0", "8", "0", "1879", "100"], ["completed cases related to drug possession", "other drugs", "ecstasy", "510", "59", "345", "40", "1", "0", "8", "1", "864", "100"], ["completed cases related to drug possession", "other drugs", "other cdsa", "6394", "62", "3884", "38", "29", "0", "40", "0", "10347", "100"], ["completed cases related to drug possession", "total completed cases related to drug possession", "", "47959", "51", "45527", "48", "221", "0", "378", "0", "94085", "100"], ["completed cases related to drug supply", "cannabis", "", "7752", "49", "7883", "50", "148", "1", "85", "1", "15868", "100"], ["completed cases related to drug supply", "other drugs", "", "15248", "56", "11600", "43", "270", "1", "111", "0", "27229", "100"], ["completed cases related to drug supply", "other drugs", "cocaine", "10968", "58", "7774", "41", "206", "1", "77", "0", "19025", "100"], ["completed cases related to drug supply", "other drugs", "heroin", "740", "58", "504", "40", "22", "2", "3", "0", "1269", "100"], ["completed cases related to drug supply", "other drugs", "methamphetamines", "371", "52", "329", "46", "5", "1", "6", "1", "711", "100"], ["completed cases related to drug supply", "other drugs", "ecstasy", "256", "52", "230", "46", "4", "1", "6", "1", "496", "100"], ["completed cases related to drug supply", "other drugs", "other cdsa", "2913", "51", "2763", "48", "33", "1", "19", "0", "5728", "100"], ["completed cases related to drug supply", "total completed cases related to drug supply", "", "23000", "53", "19483", "45", "418", "1", "196", "0", "43097", "100"], ["completed drug-related cases", "cannabis", "", "32971", "44", "41252", "55", "240", "0", "343", "0", "74806", "100"], ["completed drug-related cases", "other drugs", "", "37988", "61", "23758", "38", "399", "1", "231", "0", "62376", "100"], ["completed drug-related cases", "other drugs", "cocaine", "24838", "62", "14707", "37", "294", "1", "140", "0", "39979", "100"], ["completed drug-related cases", "other drugs", "heroin", "1483", "63", "859", "36", "26", "1", "4", "0", "2372", "100"], ["completed drug-related cases", "other drugs", "methamphetamines", "1594", "62", "970", "37", "12", "0", "14", "1", "2590", "100"], ["completed drug-related cases", "other drugs", "ecstasy", "766", "56", "575", "42", "5", "0", "14", "1", "1360", "100"], ["completed drug-related cases", "other drugs", "other cdsa", "9307", "58", "6647", "41", "62", "0", "59", "0", "16075", "100"], ["completed drug-related cases", "total completed drug-related cases", "", "70959", "52", "65010", "47", "639", "0", "574", "0", "137182", "100"]]}, "native_split": "test", "question": "broken down by type of drug, what is the percentage of cannabis supply offences with a guilty decision from 2008/2009 to 2011/2012?", "source_prompt": "{table}\n{question}", "answer_dp": "49", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_933_5372_1", "source_table_id": "933"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_1473_8057_1", "source": "hitab", "table_key": "02487b18831093fac25152754e60025353985c8b", "grid": {"TD": 1, "LD": 2, "nrows": 3, "ncols": 7, "section_rows": [], "cells": [["", "", "lowest quartile", "second quartile", "third quartile", "highest quartile", "total"], ["percent", "mobile devices only", "24.1", "14.7", "13.8", "8", "13.5"], ["percent", "personal computer", "75.9", "85.3", "86.2", "92", "86.5"]]}, "native_split": "test", "question": "what was the percentage of households in the lowest income quartile reported using only mobile devices for accessing the internet?", "source_prompt": "{table}\n{question}", "answer_dp": "24.1", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_1473_8057_1", "source_table_id": "1473"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_83_totto9457-0_0_1", "source": "hitab", "table_key": "8bc09e88682bbd5be80679d9053565d7ea34711b", "grid": {"TD": 2, "LD": 3, "nrows": 10, "ncols": 15, "section_rows": [], "cells": [["", "", "", "league", "league", "senior shield", "senior shield", "league cup", "league cup", "fa cup", "fa cup", "afc cup", "afc cup", "total", "total"], ["", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["sham shui po", "2008-09", "third 'district' division", "9", "2", "1", "0", "", "", "", "", "", "", "10", "2"], ["sham shui po", "2009-10", "third 'district' division", "2", "0", "-", "-", "", "", "", "", "", "", "2", "0"], ["sham shui po", "2010-11", "second division", "16", "4", "1", "0", "", "", "", "", "", "", "17", "4"], ["sham shui po", "2011-12", "first division", "18", "0", "2", "0", "2", "0", "2", "0", "", "", "24", "0"], ["sham shui po total", "", "", "45", "6", "4", "0", "2", "0", "2", "0", "0", "0", "53", "6"], ["yokohama fc hong kong", "2012-13", "first division", "15", "5", "1", "0", "-", "-", "1", "0", "", "", "17", "5"], ["yokohama fc hong kong total", "", "", "15", "5", "1", "0", "0", "0", "1", "0", "0", "0", "17", "5"], ["total", "", "", "60", "11", "5", "0", "2", "0", "3", "0", "0", "0", "70", "11"]]}, "native_split": "test", "question": "what was the number of league games that lee ka yiu played for sham shui po in the 2011-12 season?", "source_prompt": "{table}\n{question}", "answer_dp": "18", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_83_totto9457-0_0_1", "source_table_id": "83_totto9457-0"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_165_83_tab1_279_1", "source": "hitab", "table_key": "bae6ab411a169b97c0e6c3d94082ded735c74ac3", "grid": {"TD": 2, "LD": 3, "nrows": 23, "ncols": 6, "section_rows": [5, 12], "cells": [["", "", "", "all institutions", "survey population", "survey population"], ["", "", "", "", "short form", "standard form"], ["all r&d expenditures", "", "", "71971736", "138428", "71833308"], ["by source of funds", "federal government", "", "38860649", "67107", "38793542"], ["by source of funds", "state and local government", "", "4033779", "8499", "4025280"], ["by source of funds", "institution funds", "", "18014538", "39576", "17974962"], ["by source of funds", "institution funds", "institutionally financed research", "11505260", "34173", "11471087"], ["by source of funds", "institution funds", "cost sharing", "1432876", "2043", "1430833"], ["by source of funds", "institution funds", "unrecovered indirect costs", "5076402", "3360", "5073042"], ["by source of funds", "business", "", "4215883", "5320", "4210563"], ["by source of funds", "nonprofit organizations", "", "4628526", "13726", "4614800"], ["by source of funds", "all other sources", "", "2218361", "4200", "2214161"], ["by r&d field", "science", "", "56392202", "101540", "56290662"], ["by r&d field", "science", "computer and information sciencesa", "2082275", "4391", "2077884"], ["by r&d field", "science", "geosciences, atmospheric, and ocean sciencesa", "3095606", "7832", "3087774"], ["by r&d field", "science", "life sciences", "40933821", "45971", "40887850"], ["by r&d field", "science", "mathematics and statisticsa", "686061", "4400", "681661"], ["by r&d field", "science", "physical sciences", "4913289", "19724", "4893565"], ["by r&d field", "science", "psychology", "1222859", "4138", "1218721"], ["by r&d field", "science", "social sciences", "2374649", "8078", "2366571"], ["by r&d field", "science", "sciences, nec", "1083642", "7006", "1076636"], ["by r&d field", "engineering", "", "11394686", "12959", "11381727"], ["by r&d field", "non-s&e", "", "4184848", "23929", "4160919"]]}, "native_split": "test", "question": "how many thousand dollars did universities report current dollar r&d expenditures in fy 2016?", "source_prompt": "{table}\n{question}", "answer_dp": "71971736", "answer_type": "number", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "tolerance", "meta": {"source_local_id": "hitab_165_83_tab1_279_1", "source_table_id": "165_83_tab1"}, "dataset": "hitab", "subtype": "number"}
{"id": "hitab:hitab_448_2446_1", "source": "hitab", "table_key": "066a69ff7033ddf783b5228575b90e8f23f2373e", "grid": {"TD": 2, "LD": 2, "nrows": 11, "ncols": 10, "section_rows": [2, 7], "cells": [["", "", "sexual attack", "sexual attack", "unwanted sexual touching", "unwanted sexual touching", "sexual activity where unable to consent", "sexual activity where unable to consent", "total sexual assault", "total sexual assault"], ["", "", "number", "percent", "number", "percent", "number", "percent", "number", "percent"], ["2018", "", "", "", "", "", "", "", "", ""], ["2018", "female victims", "60", "0.8", "300", "3.7", "70", "0.9", "340", "4.3"], ["2018", "male victims", "60", "0.1", "480", "1", "50", "0.1", "520", "1.1"], ["2018", "gender diverse victims", "", "", "", "", "", "", "", ""], ["2018", "total victims", "140", "0.2", "800", "1.4", "130", "0.2", "900", "1.6"], ["2016", "", "", "", "", "", "", "", "", ""], ["2016", "female victims", "70", "0.9", "320", "4", "60", "0.7", "380", "4.8"], ["2016", "male victims", "80", "0.2", "520", "1.1", "60", "0.1", "570", "1.2"], ["2016", "total victims", "150", "0.3", "840", "1.5", "110", "0.2", "960", "1.7"]]}, "native_split": "test", "question": "in 2018, which is comparatively less common, sexual attacks or unwanted sexual touching?", "source_prompt": "{table}\n{question}", "answer_dp": "sexual attack", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_448_2446_1", "source_table_id": "448"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1004_5780_1", "source": "hitab", "table_key": "3b10f03871b8755cc2bad169fe67a72747ac8b3a", "grid": {"TD": 2, "LD": 2, "nrows": 19, "ncols": 6, "section_rows": [2, 6], "cells": [["", "", "women", "women", "men", "men"], ["", "", "non-university or university postsecondary diploma", "university degree only", "non-university or university postsecondary diploma", "university degree only"], ["region of ancestry", "", "percent", "percent", "percent", "percent"], ["region of ancestry", "caribbean and latin america", "78.1", "34.8", "59.5", "18.4"], ["region of ancestry", "africa", "79.6", "50.8", "63.6", "35.3"], ["region of ancestry", "other regions", "59.8", "19.1", "46.8", "14.1"], ["country of ancestry", "", "", "", "", ""], ["country of ancestry", "jamaica", "75.5", "31.3", "54.6", "15.8"], ["country of ancestry", "haiti", "84.5", "37.1", "65.8", "18.3"], ["country of ancestry", "trinidad and tobago", "76.1", "36.5", "61.4", "20.4"], ["country of ancestry", "barbados", "79.4", "39.2", "64.4", "22.8"], ["country of ancestry", "guyana", "74.8", "32.5", "57.6", "20.6"], ["country of ancestry", "saint vincent and the grenadines", "76.1", "35", "58.3", "17.5"], ["country of ancestry", "grenada", "78", "34.3", "62", "21.4"], ["country of ancestry", "ghana", "81.7", "49.8", "65.7", "31.4"], ["country of ancestry", "nigeria", "86.4", "63.3", "75.3", "51.5"], ["country of ancestry", "united states", "63.9", "23.2", "49.2", "17.7"], ["country of ancestry", "united kingdom", "71.9", "32", "53.9", "22.1"], ["country of ancestry", "canada", "56.5", "16", "44.8", "11.8"]]}, "native_split": "test", "question": "in 2016, which region of ancestry had a higher percentage of second-generation black women with a university degree? second-generation women with other regions of ancestry or women of african ancestry?", "source_prompt": "{table}\n{question}", "answer_dp": "africa", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1004_5780_1", "source_table_id": "1004"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_74_3_nsf21317-tab003_21_1", "source": "hitab", "table_key": "fc538fbf3cfcf09c3f4c96eecd4afc0b803d8e00", "grid": {"TD": 1, "LD": 4, "nrows": 29, "ncols": 8, "section_rows": [1, 4, 5, 16, 26], "cells": [["", "", "", "", "2017", "2018", "2019", "percent change 2017-19"], ["all surveyed fields", "", "", "", "28180", "29284", "30349", "7.7"], ["all surveyed fields", "male", "", "", "16580", "17468", "17980", "8.4"], ["all surveyed fields", "female", "", "", "11600", "11816", "12369", "6.6"], ["all surveyed fields", "science and engineering", "", "", "20542", "21848", "22728", "10.6"], ["all surveyed fields", "science and engineering", "science", "", "17268", "18278", "18819", "9"], ["all surveyed fields", "science and engineering", "science", "agricultural sciences", "496", "565", "645", "30"], ["all surveyed fields", "science and engineering", "science", "biological and biomedical sciences", "8203", "8250", "8229", "0.3"], ["all surveyed fields", "science and engineering", "science", "computer and information sciences", "476", "515", "510", "7.1"], ["all surveyed fields", "science and engineering", "science", "geosciences, atmospheric sciences, and ocean sciences", "1794", "2106", "2177", "21.3"], ["all surveyed fields", "science and engineering", "science", "mathematics and statistics", "240", "266", "305", "27.1"], ["all surveyed fields", "science and engineering", "science", "multidisciplinary and interdisciplinary studies", "806", "832", "820", "1.7"], ["all surveyed fields", "science and engineering", "science", "natural resources and conservation", "364", "580", "582", "59.9"], ["all surveyed fields", "science and engineering", "science", "physical sciences", "2871", "3056", "3316", "15.5"], ["all surveyed fields", "science and engineering", "science", "psychology", "494", "507", "576", "16.6"], ["all surveyed fields", "science and engineering", "science", "social sciences", "1524", "1601", "1659", "8.9"], ["all surveyed fields", "science and engineering", "engineering", "", "3274", "3570", "3909", "19.4"], ["all surveyed fields", "science and engineering", "engineering", "bioengineering and biomedical engineering", "415", "440", "492", "18.6"], ["all surveyed fields", "science and engineering", "engineering", "chemical engineering", "281", "257", "328", "16.7"], ["all surveyed fields", "science and engineering", "engineering", "civil engineering", "422", "414", "492", "16.6"], ["all surveyed fields", "science and engineering", "engineering", "electrical, electronics, and communications engineering", "557", "588", "637", "14.4"], ["all surveyed fields", "science and engineering", "engineering", "engineering science, mechanics, and physics", "200", "220", "186", "-7"], ["all surveyed fields", "science and engineering", "engineering", "industrial and manufacturing engineering", "119", "105", "137", "15.1"], ["all surveyed fields", "science and engineering", "engineering", "mechanical engineering", "458", "489", "531", "15.9"], ["all surveyed fields", "science and engineering", "engineering", "metallurgical and materials engineering", "181", "215", "242", "33.7"], ["all surveyed fields", "science and engineering", "engineering", "other engineeringa", "641", "842", "864", "34.8"], ["all surveyed fields", "health", "", "", "7638", "7436", "7621", "-0.2"], ["all surveyed fields", "health", "clinical medicine", "", "6448", "6159", "6273", "-2.7"], ["all surveyed fields", "health", "other health", "", "1190", "1277", "1348", "13.3"]]}, "native_split": "test", "question": "rates of growth in the number of nfrs between 2017 and 2019 in various s&e fields of research varied widely, which broad field had the largest percentage of increasing?", "source_prompt": "{table}\n{question}", "answer_dp": "natural resources and conservation", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_74_3_nsf21317-tab003_21_1", "source_table_id": "74_3_nsf21317-tab003"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_299_totto34472-0_0_1", "source": "hitab", "table_key": "5dd2c0b2822efa6862d243734a1deb64efbf45fb", "grid": {"TD": 2, "LD": 1, "nrows": 20, "ncols": 16, "section_rows": [], "cells": [["", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week", "week"], ["", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "finale"], ["bill", "6.29", "3.83", "1", "4.36", "2.11", "3.94", "2.61", "3.83", "3.59", "3.31", "2.56", "2.63", "4.05", "4.22", "16.67"], ["julie", "0.92", "1.39", "1.41", "3.33", "1.89", "2.01", "1.51", "3.13", "3.23", "0", "2.22", "2.84", "1.75", "4.76", "24.38"], ["hollie", "4.31", "0.82", "1.24", "1.28", "3.39", "1.32", "0.89", "2.24", "2.75", "0", "3.77", "1.47", "2.49", "2.35", "21.47"], ["isabeau", "2.68", "1.72", "1.75", "2.86", "2.94", "1.89", "1.54", "0", "3.53", "2.03", "2.9", "1.71", "2.17", "3.56", "14.75"], ["neil", "5.23", "2.51", "0.77", "2.85", "2.67", "2.74", "4.79", "8.87", "5.31", "3.12", "2.57", "1.65", "3.34", "3.47", "24.26"], ["nicole", "2.87", "2.21", "1.13", "1.53", "2.33", "1.59", "1.61", "3.69", "2.55", "3.06", "1.8", "2.75", "0.47", "17.54", "17.54"], ["bryan", "4.34", "1.81", "1.23", "0.62", "3.45", "1.3", "2.3", "2.69", "3.81", "0.72", "2.17", "1.48", "15.79", "15.79", "15.79"], ["kae", "8.89", "2.44", "1.5", "3.05", "3.66", "3.26", "2.25", "3.45", "2.98", "2.45", "1.89", "17.95", "17.95", "17.95", "17.95"], ["amy", "3.37", "0.7", "2.11", "0.36", "3.6", "1.12", "0", "4.15", "3.54", "1.22", "29.34", "29.34", "29.34", "29.34", "29.34"], ["ryan", "4.01", "2.79", "0.57", "1.73", "2.93", "3.32", "0.31", "4.05", "1.95", "20.2", "20.2", "20.2", "20.2", "20.2", "20.2"], ["david", "5.43", "1.15", "1.74", "0.3", "2.67", "1.52", "2.48", "0.95", "26.84", "26.84", "26.84", "26.84", "26.84", "26.84", "26.84"], ["jez", "4.64", "2.74", "1.25", "2.53", "1.95", "2.32", "2.03", "32.58", "32.58", "32.58", "32.58", "32.58", "32.58", "32.58", "32.58"], ["phil", "6.45", "1.33", "2.42", "1.38", "3.35", "1.16", "24.56", "24.56", "24.56", "24.56", "24.56", "24.56", "24.56", "24.56", "24.56"], ["jim", "3.05", "4.57", "2.69", "3.38", "2.23", "43", "43", "43", "43", "43", "43", "43", "43", "43", "43"], ["patty", "4.64", "0.75", "1.89", "1.92", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29", "15.29"], ["jerry", "10.44", "3.76", "0.78", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38", "26.38"], ["lezlye", "3.53", "1.22", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7", "17.7"], ["amber", "2.37", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74", "26.74"]]}, "native_split": "train", "question": "in week 12, who was the biggest loser with a percentage weight loss of 2.84%?", "source_prompt": "{table}\n{question}", "answer_dp": "julie", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_299_totto34472-0_0_1", "source_table_id": "299_totto34472-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_764_4349_1", "source": "hitab", "table_key": "e05374ff694a245b761bf47804487da6f0257c42", "grid": {"TD": 2, "LD": 3, "nrows": 17, "ncols": 9, "section_rows": [2, 5, 8, 11, 14], "cells": [["", "", "", "total", "total", "canadian-born", "canadian-born", "immigrant", "immigrant"], ["", "", "", "women", "men", "women", "men", "women", "men"], ["percentage", "age 15 and over", "", "", "", "", "", "", ""], ["percentage", "age 15 and over", "visible minority", "10.6", "9.3", "11.4", "12.3", "10.5", "8.5"], ["percentage", "age 15 and over", "not a visible minority", "6.7", "7.8", "6.7", "7.9", "6.7", "6.1"], ["percentage", "age 15 to 24", "", "", "", "", "", "", ""], ["percentage", "age 15 to 24", "visible minority", "19.5", "21.5", "19.1", "22.4", "19.8", "20.5"], ["percentage", "age 15 to 24", "not a visible minority", "14.5", "16.9", "14.5", "16.9", "15.3", "17.2"], ["percentage", "age 25 to 54", "", "", "", "", "", "", ""], ["percentage", "age 25 to 54", "visible minority", "9.3", "7.2", "6.7", "6.7", "9.8", "7.3"], ["percentage", "age 25 to 54", "not a visible minority", "5.3", "6", "5.2", "6.1", "6.5", "5.4"], ["percentage", "age 55 to 64", "", "", "", "", "", "", ""], ["percentage", "age 55 to 64", "visible minority", "8.2", "8.1", "4.1", "7.5", "8.3", "8.1"], ["percentage", "age 55 to 64", "not a visible minority", "5.3", "6.9", "5.3", "7", "5.3", "6"], ["percentage", "age 65 and over", "", "", "", "", "", "", ""], ["percentage", "age 65 and over", "visible minority", "10.2", "7.8", "4.3", "5", "10.3", "8"], ["percentage", "age 65 and over", "not a visible minority", "7.4", "6.5", "7.7", "6.9", "6.6", "5.1"]]}, "native_split": "test", "question": "within the canadian-born population, which visible minority gender was slightly less likely be unemployed overall, women or men?", "source_prompt": "{table}\n{question}", "answer_dp": "women", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_764_4349_1", "source_table_id": "764"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2404_12869_1", "source": "hitab", "table_key": "d0befd903c31d85efb16a9dffe73148af846cdc2", "grid": {"TD": 1, "LD": 3, "nrows": 46, "ncols": 5, "section_rows": [1, 7, 13, 16, 22, 25, 29, 32, 35, 40], "cells": [["", "", "", "univariate models", "multivariate model"], ["odds ratios", "province of residence", "", "", ""], ["odds ratios", "province of residence", "atlantic", "1.14", "0.92"], ["odds ratios", "province of residence", "quebec", "0.7", "0.8"], ["odds ratios", "province of residence", "ontario", "1", "1"], ["odds ratios", "province of residence", "prairies", "0.84", "0.72"], ["odds ratios", "province of residence", "british columbia", "1.09", "0.91"], ["odds ratios", "age group", "", "", ""], ["odds ratios", "age group", "under 20 years", "1.91", "1.77"], ["odds ratios", "age group", "20 to 24 years", "2.14", "1.95"], ["odds ratios", "age group", "25 to 29 years", "2.08", "1.88"], ["odds ratios", "age group", "30 to 34 years", "1", "1"], ["odds ratios", "age group", "35 years and over", "1.3", "1.35"], ["odds ratios", "sex", "", "", ""], ["odds ratios", "sex", "male", "1.12", "1.06"], ["odds ratios", "sex", "female", "1", "1"], ["odds ratios", "region of birth", "", "", ""], ["odds ratios", "region of birth", "europe", "0.92", "0.69"], ["odds ratios", "region of birth", "africa", "0.73", "0.86"], ["odds ratios", "region of birth", "asia or oceania", "0.83", "0.77"], ["odds ratios", "region of birth", "united states", "1", "1"], ["odds ratios", "region of birth", "central or south america", "0.57", "0.63"], ["odds ratios", "de facto marital status on census day", "", "", ""], ["odds ratios", "de facto marital status on census day", "single, widowed, separated or divorced", "1.85", "1.64"], ["odds ratios", "de facto marital status on census day", "married or common-law", "1", "1"], ["odds ratios", "mother tongue on census day", "", "", ""], ["odds ratios", "mother tongue on census day", "english", "1", "1"], ["odds ratios", "mother tongue on census day", "french", "0.66", "0.77"], ["odds ratios", "mother tongue on census day", "non-official language", "0.84", "0.98"], ["odds ratios", "first npr permit", "", "", ""], ["odds ratios", "first npr permit", "no", "1", "1"], ["odds ratios", "first npr permit", "yes", "1.19", "0.6"], ["odds ratios", "holds more than one permit", "", "", ""], ["odds ratios", "holds more than one permit", "no", "1", "1"], ["odds ratios", "holds more than one permit", "yes", "0.78", "0.84"], ["odds ratios", "length of stay in canada", "", "", ""], ["odds ratios", "length of stay in canada", "0 to 6 months", "1.98", "2.24"], ["odds ratios", "length of stay in canada", "6 to 12 months", "1.78", "2.09"], ["odds ratios", "length of stay in canada", "12 to 24 months", "1.07", "1.11"], ["odds ratios", "length of stay in canada", "24 months or more", "1", "1"], ["odds ratios", "type of permit", "", "", ""], ["odds ratios", "type of permit", "refugee status claimant", "0.54", "0.53"], ["odds ratios", "type of permit", "work permit", "1", "1"], ["odds ratios", "type of permit", "study or minister's permit", "1.01", "0.77"], ["percent", "r-squared", "", "", "7"], ["number", "number of observations", "", "", "937"]]}, "native_split": "test", "question": "who were less likely to be missed, nprs whose temporary residence permit was their first permit or nprs who had obtained other permits in the past?", "source_prompt": "{table}\n{question}", "answer_dp": "yes", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2404_12869_1", "source_table_id": "2404"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1631_9237_1", "source": "hitab", "table_key": "c58a3177dabb71cf03eb57dbc055c142df8bc540", "grid": {"TD": 2, "LD": 3, "nrows": 8, "ncols": 11, "section_rows": [2], "cells": [["", "", "", "total - 65 years and over", "total - 65 years and over", "65 to 74 years", "65 to 74 years", "75 to 84 years", "75 to 84 years", "85 years and over", "85 years and over"], ["", "", "", "women", "men", "women", "men", "women", "men", "women", "men"], ["percentage", "total", "", "100", "100", "100", "100", "100", "100", "100", "100"], ["percentage", "total", "spouse", "19", "60", "36", "71", "20", "63", "", "36"], ["percentage", "total", "son", "19", "10", "17", "7", "20", "11", "20", "15"], ["percentage", "total", "daughter", "42", "15", "20", "", "44", "11", "57", "33"], ["percentage", "total", "close friend", "4", "4", "6", "", "5", "", "", ""], ["percentage", "total", "other family member, friend, or acquaintance", "16", "10", "21", "8", "11", "12", "17", ""]]}, "native_split": "test", "question": "what's the commonly reported primary caregiver among senior women aged 65 to 74.", "source_prompt": "{table}\n{question}", "answer_dp": "spouse", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1631_9237_1", "source_table_id": "1631"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_350_1968_2", "source": "hitab", "table_key": "609ee786c10d3da510912116b89f679ec467b4c2", "grid": {"TD": 1, "LD": 3, "nrows": 9, "ncols": 5, "section_rows": [1, 5], "cells": [["", "", "", "2003", "2013-2014"], ["percent", "12 to 44", "", "16", "19"], ["percent", "12 to 44", "12 to 17", "18", "23"], ["percent", "12 to 44", "18 to 34", "13", "17"], ["percent", "12 to 44", "35 to 44", "18", "22"], ["percent", "45 to 64", "", "32", "32"], ["percent", "45 to 64", "45 to 54", "26", "25"], ["percent", "45 to 64", "55 to 64", "39", "39"], ["percent", "65 and over", "", "67", "64"]]}, "native_split": "test", "question": "in 2003, what was the percentage of seniors got a flu vaccination?", "source_prompt": "{table}\n{question}", "answer_dp": "65 and over", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_350_1968_2", "source_table_id": "350"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1123_6365_1", "source": "hitab", "table_key": "afef2dc246c80fb6152c5fcb670c8fc35f16ed8f", "grid": {"TD": 3, "LD": 2, "nrows": 16, "ncols": 11, "section_rows": [3], "cells": [["", "", "19 to 49 years", "19 to 49 years", "19 to 49 years", "50 to 69 years", "50 to 69 years", "50 to 69 years", "70 years and older", "70 years and older", "70 years and older"], ["", "", "linked cctlp tumour cohort", "tumours with one or more surgical intervention", "tumours with one or more surgical intervention", "linked cctlp tumour cohort", "tumours with one or more surgical intervention", "tumours with one or more surgical intervention", "linked cctlp tumour cohort", "tumours with one or more surgical intervention", "tumours with one or more surgical intervention"], ["", "", "number", "number", "percent", "number", "number", "percent", "number", "number", "percent"], ["canada", "", "6070", "5745", "94.7", "5120", "4775", "93.3", "1360", "1170", "86.1"], ["canada", "newfoundland and labrador", "125", "125", "100", "145", "145", "98.6", "45", "40", "93.3"], ["canada", "prince edward island", "10", "10", "100", "15", "10", "80", "5", "5", "100"], ["canada", "nova scotia", "140", "135", "96.4", "160", "160", "98.8", "55", "50", "92.9"], ["canada", "new brunswick", "140", "145", "100", "215", "215", "98.6", "50", "45", "81.1"], ["canada", "ontario", "4095", "3855", "94.1", "3380", "3115", "92.2", "830", "715", "86.5"], ["canada", "manitoba", "185", "170", "90.9", "145", "140", "94.6", "45", "40", "84.4"], ["canada", "saskatchewan", "135", "135", "98.5", "105", "100", "96.2", "35", "30", "80"], ["canada", "alberta", "720", "695", "96.7", "495", "470", "94.5", "125", "105", "85.5"], ["canada", "british columbia", "505", "470", "93.5", "450", "420", "92.9", "165", "140", "83.1"], ["canada", "yukon", "", "", "0", "", "", "100", "", "", "100"], ["canada", "northwest territories", "", "", "0", "", "", "100", "0", "0", "0"], ["canada", "nunavut", "", "", "0", "", "", "100", "", "", "0"]]}, "native_split": "test", "question": "which age group has the lowest surgical rate?", "source_prompt": "{table}\n{question}", "answer_dp": "70 years and older", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1123_6365_1", "source_table_id": "1123"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_434_totto50055-3_0_1", "source": "hitab", "table_key": "708fc45e995bad3206b7fed3820f7d0f953826cd", "grid": {"TD": 2, "LD": 2, "nrows": 32, "ncols": 11, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "continental", "continental", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["ajax", "1992-93", "eredivisie", "12", "1", "3", "0", "3", "0", "18", "1"], ["ajax", "1993-94", "eredivisie", "19", "4", "3", "0", "2", "0", "24", "4"], ["ajax", "1994-95", "eredivisie", "34", "6", "3", "0", "11", "0", "48", "6"], ["ajax", "total", "", "65", "11", "9", "0", "16", "0", "90", "11"], ["sampdoria", "1995-96", "serie a", "32", "3", "2", "1", "-", "-", "34", "4"], ["sampdoria", "total", "", "32", "3", "2", "1", "-", "-", "34", "4"], ["real madrid", "1996-97", "la liga", "38", "6", "4", "0", "-", "-", "42", "6"], ["real madrid", "1997-98", "la liga", "36", "6", "2", "1", "11", "0", "49", "7"], ["real madrid", "1998-99", "la liga", "37", "3", "5", "1", "10", "3", "52", "7"], ["real madrid", "1999-2000", "la liga", "10", "0", "0", "0", "6", "0", "16", "0"], ["real madrid", "total", "", "121", "15", "11", "2", "27", "3", "159", "20"], ["inter milan", "1999-2000", "serie a", "20", "3", "5", "2", "0", "0", "25", "5"], ["inter milan", "2000-01", "serie a", "24", "2", "5", "0", "7", "3", "36", "5"], ["inter milan", "2001-02", "serie a", "20", "3", "2", "1", "10", "0", "32", "4"], ["inter milan", "total", "", "64", "8", "12", "3", "17", "3", "93", "14"], ["milan", "2002-03", "serie a", "29", "4", "3", "2", "16", "1", "48", "7"], ["milan", "2003-04", "serie a", "29", "3", "6", "0", "10", "0", "45", "3"], ["milan", "2004-05", "serie a", "32", "5", "4", "1", "13", "1", "49", "7"], ["milan", "2005-06", "serie a", "36", "4", "2", "1", "11", "1", "49", "6"], ["milan", "2006-07", "serie a", "32", "7", "5", "0", "14", "3", "51", "10"], ["milan", "2007-08", "serie a", "32", "7", "0", "0", "10", "3", "42", "10"], ["milan", "2008-09", "serie a", "33", "6", "1", "0", "7", "0", "41", "6"], ["milan", "2009-10", "serie a", "29", "5", "0", "0", "8", "1", "37", "6"], ["milan", "2010-11", "serie a", "30", "4", "2", "0", "8", "0", "40", "4"], ["milan", "2011-12", "serie a", "18", "2", "3", "1", "8", "0", "29", "3"], ["milan", "total", "", "300", "48", "26", "5", "105", "10", "431", "63"], ["botafogo", "2012", "serie a", "24", "8", "0", "0", "0", "0", "24", "8"], ["botafogo", "2013", "serie a", "34", "8", "0", "0", "1", "1", "35", "9"], ["botafogo", "total", "", "58", "16", "0", "0", "1", "1", "59", "17"], ["career total", "", "", "640", "100", "69", "11", "166", "17", "875", "128"]]}, "native_split": "test", "question": "which league did clarence seedorf play for milan in the 2011-12?", "source_prompt": "{table}\n{question}", "answer_dp": "serie a", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_434_totto50055-3_0_1", "source_table_id": "434_totto50055-3"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_764_4353_1", "source": "hitab", "table_key": "e05374ff694a245b761bf47804487da6f0257c42", "grid": {"TD": 2, "LD": 3, "nrows": 17, "ncols": 9, "section_rows": [2, 5, 8, 11, 14], "cells": [["", "", "", "total", "total", "canadian-born", "canadian-born", "immigrant", "immigrant"], ["", "", "", "women", "men", "women", "men", "women", "men"], ["percentage", "age 15 and over", "", "", "", "", "", "", ""], ["percentage", "age 15 and over", "visible minority", "10.6", "9.3", "11.4", "12.3", "10.5", "8.5"], ["percentage", "age 15 and over", "not a visible minority", "6.7", "7.8", "6.7", "7.9", "6.7", "6.1"], ["percentage", "age 15 to 24", "", "", "", "", "", "", ""], ["percentage", "age 15 to 24", "visible minority", "19.5", "21.5", "19.1", "22.4", "19.8", "20.5"], ["percentage", "age 15 to 24", "not a visible minority", "14.5", "16.9", "14.5", "16.9", "15.3", "17.2"], ["percentage", "age 25 to 54", "", "", "", "", "", "", ""], ["percentage", "age 25 to 54", "visible minority", "9.3", "7.2", "6.7", "6.7", "9.8", "7.3"], ["percentage", "age 25 to 54", "not a visible minority", "5.3", "6", "5.2", "6.1", "6.5", "5.4"], ["percentage", "age 55 to 64", "", "", "", "", "", "", ""], ["percentage", "age 55 to 64", "visible minority", "8.2", "8.1", "4.1", "7.5", "8.3", "8.1"], ["percentage", "age 55 to 64", "not a visible minority", "5.3", "6.9", "5.3", "7", "5.3", "6"], ["percentage", "age 65 and over", "", "", "", "", "", "", ""], ["percentage", "age 65 and over", "visible minority", "10.2", "7.8", "4.3", "5", "10.3", "8"], ["percentage", "age 65 and over", "not a visible minority", "7.4", "6.5", "7.7", "6.9", "6.6", "5.1"]]}, "native_split": "test", "question": "which gender's the unemployment rate of immigrant visible minority was higher, women or men?", "source_prompt": "{table}\n{question}", "answer_dp": "women", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_764_4353_1", "source_table_id": "764"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_44_totto4750-0_0_1", "source": "hitab", "table_key": "7a9d040cead860bf677958a68e29ca5249d3fee7", "grid": {"TD": 2, "LD": 2, "nrows": 31, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "league cup", "league cup", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["gretna", "2002-03", "scottish third division", "8", "0", "0", "0", "0", "0", "0", "0", "8", "0"], ["gretna", "2003-04", "scottish third division", "1", "0", "0", "0", "0", "0", "0", "0", "1", "0"], ["gretna", "2004-05", "scottish third division", "3", "0", "0", "0", "0", "0", "0", "0", "3", "0"], ["gretna", "2005-06", "scottish second division", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0"], ["gretna", "2006-07", "scottish first division", "30", "2", "2", "0", "1", "0", "1", "0", "34", "2"], ["gretna", "2007-08", "scottish premier league", "10", "1", "0", "0", "2", "0", "-", "-", "12", "1"], ["gretna", "total", "", "52", "3", "3", "0", "3", "0", "1", "0", "59", "3"], ["brechin city", "2005-06", "scottish first division", "10", "0", "0", "0", "0", "0", "0", "0", "10", "0"], ["brechin city", "total", "", "10", "0", "0", "0", "0", "0", "0", "0", "10", "0"], ["dundee united", "2007-08", "scottish premier league", "14", "0", "2", "0", "0", "0", "-", "-", "16", "0"], ["dundee united", "2008-09", "scottish premier league", "9", "0", "2", "1", "1", "0", "-", "-", "12", "1"], ["dundee united", "total", "", "23", "0", "4", "1", "1", "0", "0", "0", "28", "1"], ["st johnstone", "2009-10", "scottish premier league", "36", "1", "2", "0", "4", "0", "-", "-", "42", "1"], ["st johnstone", "2010-11", "scottish premier league", "33", "2", "5", "0", "3", "0", "-", "-", "41", "2"], ["st johnstone", "total", "", "69", "3", "7", "0", "7", "0", "0", "0", "83", "3"], ["hearts", "2011-12", "scottish premier league", "27", "0", "6", "1", "1", "0", "4", "0", "38", "1"], ["hearts", "2012-13", "scottish premier league", "13", "2", "0", "0", "2", "1", "2", "0", "17", "3"], ["hearts", "total", "", "40", "2", "6", "1", "3", "1", "6", "0", "55", "4"], ["st mirren", "2013-14", "scottish premiership", "13", "0", "1", "0", "1", "0", "-", "-", "15", "0"], ["st mirren", "total", "", "13", "0", "1", "0", "1", "0", "0", "0", "15", "0"], ["dunfermline", "2013-14", "scottish league one", "12", "2", "0", "0", "0", "0", "4", "0", "16", "2"], ["dunfermline", "total", "", "12", "2", "0", "0", "0", "0", "4", "0", "16", "2"], ["carlisle", "2014-15", "league two", "41", "3", "0", "0", "1", "0", "1", "0", "43", "3"], ["carlisle", "2015-16", "league two", "36", "5", "4", "2", "3", "0", "1", "0", "44", "7"], ["carlisle", "2016-17", "league two", "31", "6", "2", "1", "2", "0", "6", "2", "41", "9"], ["carlisle", "2017-18", "league two", "34", "8", "5", "1", "2", "1", "3", "0", "44", "10"], ["carlisle", "2018-19", "league two", "23", "5", "1", "0", "0", "0", "1", "0", "24", "5"], ["carlisle", "total", "", "165", "27", "12", "4", "8", "1", "12", "2", "197", "34"], ["career total", "", "", "384", "37", "33", "6", "23", "2", "23", "2", "463", "47"]]}, "native_split": "test", "question": "in the 2007-08 season, which leage division did grainger play in?", "source_prompt": "{table}\n{question}", "answer_dp": "scottish premier league", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_44_totto4750-0_0_1", "source_table_id": "44_totto4750-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_491_2677_1", "source": "hitab", "table_key": "535289414e454905de53b18849b9ff49409e8c21", "grid": {"TD": 2, "LD": 2, "nrows": 21, "ncols": 5, "section_rows": [2, 5, 8, 11, 14, 17], "cells": [["", "", "%", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to"], ["discomfort or injury involving a laser product", "", "", "", ""], ["discomfort or injury involving a laser product", "yes", "1.1", "0.7", "1.4"], ["discomfort or injury involving a laser product", "no", "98.9", "98.5", "99.2"], ["discomfort or injury involving laser product-number of times", "", "", "", ""], ["discomfort or injury involving laser product-number of times", "1 time", "44.9", "27.6", "62.1"], ["discomfort or injury involving laser product-number of times", "2 to 260 times", "49.5", "32.2", "66.7"], ["type of discomfort or injury", "", "", "", ""], ["type of discomfort or injury", "skin", "41.3", "24.1", "58.5"], ["type of discomfort or injury", "eye", "59.1", "41.9", "76.3"], ["length of discomfort or injury", "", "", "", ""], ["length of discomfort or injury", "two days or less", "63.9", "45.2", "82.6"], ["length of discomfort or injury", "more than two days", "34", "15.2", "52.9"], ["own use or someone else's", "", "", "", ""], ["own use or someone else's", "your own use of the device", "25", "12.1", "37.8"], ["own use or someone else's", "someone else's use of the device", "74.9", "62", "87.7"], ["type of laser that caused injury, excluding medical devices", "", "", "", ""], ["type of laser that caused injury, excluding medical devices", "pointer", "26.3", "11.7", "40.9"], ["type of laser that caused injury, excluding medical devices", "cosmetic treatments", "39.1", "21.7", "56.6"], ["type of laser that caused injury, excluding medical devices", "survey tool / entertainment / materials processing / scanner / other", "34.1", "18.4", "49.8"]]}, "native_split": "test", "question": "what caused the majority of injuries?", "source_prompt": "{table}\n{question}", "answer_dp": "cosmetic treatments", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_491_2677_1", "source_table_id": "491"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2480_13245_1", "source": "hitab", "table_key": "dfb10b3a2656dff7ad1f9b45f38467868f9d9b0e", "grid": {"TD": 2, "LD": 2, "nrows": 16, "ncols": 5, "section_rows": [2], "cells": [["", "", "ratio of listings to dwellings", "daily revenue per available listing", "daily revenue per available room"], ["", "", "%", "dollars", "dollars"], ["canada", "", "0.9", "37.2", "94.82"], ["canada", "newfoundland and labrador", "0.4", "25.77", "90.45"], ["canada", "prince edward island", "1.4", "49.06", "77.43"], ["canada", "nova scotia", "0.6", "40.97", "84.36"], ["canada", "new brunswick", "0.3", "26.26", "66.36"], ["canada", "quebec", "1.2", "31.16", "108.34"], ["canada", "ontario", "0.8", "36.61", "98.79"], ["canada", "manitoba", "0.2", "17.78", "76.63"], ["canada", "saskatchewan", "0.2", "14.36", "67.67"], ["canada", "alberta", "0.5", "23.83", "68.81"], ["canada", "british columbia", "1.7", "50.55", "111.45"], ["canada", "yukon", "1.4", "25.32", ""], ["canada", "northwest territories", "0.8", "41.06", ""], ["canada", "nunavut", "0.2", "12.34", ""]]}, "native_split": "test", "question": "which country had the highest proportion of listings relative to total dwellings?", "source_prompt": "{table}\n{question}", "answer_dp": "british columbia", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2480_13245_1", "source_table_id": "2480"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_513_totto58801-4_0_1", "source": "hitab", "table_key": "6a0ffda27c6f306428c559b896bbbccfb8591f6e", "grid": {"TD": 2, "LD": 2, "nrows": 20, "ncols": 8, "section_rows": [], "cells": [["", "", "race 1", "race 1", "race 2", "race 2", "final", "final"], ["", "", "time", "rank", "time", "rank", "time", "rank"], ["500 m", "jan bos", "36.149", "31", "36.111", "29", "72.26", "29"], ["500 m", "simon kuipers", "35.662", "23", "35.669", "20", "71.33", "20"], ["500 m", "ronald mulder", "35.155", "11", "35.146", "10", "70.3", "11"], ["500 m", "jan smeekens", "35.16", "12", "35.051", "4", "70.21", "6"], ["1000 m", "jan bos", "none", "none", "none", "none", "1:10.29", "12"], ["1000 m", "stefan groothuis", "none", "none", "none", "none", "1:09.45", "4"], ["1000 m", "simon kuipers", "none", "none", "none", "none", "1:09.65", "6"], ["1000 m", "mark tuitert", "none", "none", "none", "none", "1:09.48", "5"], ["1500 m", "stefan groothuis", "none", "none", "none", "none", "1:48.03", "16"], ["1500 m", "sven kramer", "none", "none", "none", "none", "1:47.40", "13"], ["1500 m", "simon kuipers", "none", "none", "none", "none", "1:46.76", "7"], ["1500 m", "mark tuitert", "none", "none", "none", "none", "1:45.57 tr", "none"], ["5000 m", "jan blokhuijsen", "none", "none", "none", "none", "6:26.30", "9"], ["5000 m", "bob de jong", "none", "none", "none", "none", "6:19.02", "5"], ["5000 m", "sven kramer", "none", "none", "none", "none", "6:14.60 or", "none"], ["10,000 m", "bob de jong", "none", "none", "none", "none", "13:06.73", "none"], ["10,000 m", "arjen van der kieft", "none", "none", "none", "none", "13:33.37", "9"], ["10,000 m", "sven kramer", "none", "none", "none", "none", "dsq", "-"]]}, "native_split": "test", "question": "what the time did sven kramer set an olympic record in the 5000m men's speed skating event?", "source_prompt": "{table}\n{question}", "answer_dp": "6:14.60 or", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_513_totto58801-4_0_1", "source_table_id": "513_totto58801-4"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_63_totto7285-3_0_1", "source": "hitab", "table_key": "8dc136451436bdc042c1ab18dcae0b0b0d9674c3", "grid": {"TD": 2, "LD": 3, "nrows": 28, "ncols": 13, "section_rows": [], "cells": [["", "", "", "league", "league", "fa cup", "fa cup", "league cup", "league cup", "other", "other", "total", "total"], ["", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["wolverhampton wanderers", "2004-05", "championship", "1", "0", "0", "0", "0", "0", "0", "0", "1", "0"], ["rushden & diamonds", "2004-05", "league two", "13", "3", "0", "0", "0", "0", "0", "0", "13", "3"], ["sheffield united", "2005-06", "championship", "0", "0", "0", "0", "2", "0", "0", "0", "2", "0"], ["port vale", "2005-06", "league one", "10", "1", "1", "0", "0", "0", "1", "0", "12", "1"], ["gillingham", "2005-06", "league one", "13", "1", "0", "0", "0", "0", "0", "0", "13", "1"], ["gillingham", "2006-07", "league one", "38", "7", "2", "1", "1", "0", "1", "1", "42", "9"], ["gillingham", "2007-08", "league one", "30", "5", "1", "0", "1", "0", "2", "0", "34", "5"], ["gillingham", "2008-09", "league two", "26", "2", "4", "0", "1", "0", "0", "0", "31", "2"], ["gillingham", "total", "", "107", "15", "7", "1", "3", "0", "3", "1", "120", "17"], ["northampton town", "2009-10", "league two", "9", "0", "0", "0", "0", "0", "3", "0", "12", "0"], ["gateshead", "2010-11", "conference national", "31", "7", "2", "0", "0", "0", "2", "0", "35", "7"], ["gateshead", "2011-12", "conference national", "14", "0", "0", "0", "0", "0", "1", "0", "15", "0"], ["gateshead", "total", "", "45", "7", "2", "0", "0", "0", "3", "0", "50", "7"], ["brackley town", "2011-12", "southern league premier division", "12", "2", "0", "0", "0", "0", "0", "0", "12", "2"], ["brackley town", "2012-13", "conference north", "28", "4", "0", "0", "0", "0", "1", "0", "29", "4"], ["brackley town", "2013-14", "conference north", "26", "0", "5", "0", "0", "0", "0", "0", "31", "0"], ["brackley town", "2014-15", "conference north", "6", "0", "0", "0", "0", "0", "0", "0", "6", "0"], ["brackley town", "total", "", "72", "6", "5", "0", "0", "0", "1", "0", "78", "6"], ["dunstable town", "2014-15", "southern league premier division", "16", "2", "3", "0", "0", "0", "1", "0", "20", "2"], ["kettering town", "2014-15", "southern league division one central", "13", "1", "0", "0", "0", "0", "1", "0", "14", "1"], ["kettering town", "2015-16", "southern league premier division", "39", "2", "3", "0", "0", "0", "3", "1", "45", "3"], ["kettering town", "2016-17", "southern league premier division", "40", "0", "4", "0", "0", "0", "3", "0", "47", "0"], ["kettering town", "2017-18", "southern league premier division", "40", "1", "5", "0", "0", "0", "4", "0", "49", "1"], ["kettering town", "total", "", "132", "4", "12", "0", "0", "0", "11", "1", "155", "5"], ["corby town", "2018-19", "southern league division one central", "37", "2", "5", "0", "0", "0", "1", "0", "43", "2"], ["career totals", "", "", "439", "40", "35", "1", "5", "0", "24", "2", "503", "43"]]}, "native_split": "test", "question": "which division did gary mulligan sign for kettering town in 2014-15?", "source_prompt": "{table}\n{question}", "answer_dp": "southern league division one central", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_63_totto7285-3_0_1", "source_table_id": "63_totto7285-3"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_177_totto20947-0_0_1", "source": "hitab", "table_key": "be56708c1562b62c3a1407f9add5f62325f56cc5", "grid": {"TD": 2, "LD": 3, "nrows": 16, "ncols": 11, "section_rows": [], "cells": [["", "", "", "league", "league", "cup", "cup", "europe", "europe", "total", "total"], ["", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["sarpsborg 08", "2011", "tippeligaen", "9", "0", "1", "0", "-", "-", "10", "0"], ["sarpsborg 08", "2012", "1. divisjon", "26", "9", "3", "1", "-", "-", "29", "10"], ["sarpsborg 08", "2013", "tippeligaen", "29", "6", "1", "0", "-", "-", "30", "6"], ["sarpsborg 08", "total", "", "64", "15", "5", "1", "0", "0", "69", "16"], ["molde", "2014", "tippeligaen", "30", "13", "4", "4", "3", "0", "37", "17"], ["molde", "2015", "tippeligaen", "28", "12", "1", "0", "12", "7", "41", "19"], ["molde", "2016", "tippeligaen", "12", "5", "1", "1", "2", "0", "15", "6"], ["molde", "total", "", "70", "30", "6", "5", "17", "7", "93", "42"], ["basel", "2016-17", "swiss super league", "32", "10", "4", "0", "3", "0", "39", "10"], ["basel", "2017-18", "swiss super league", "34", "12", "3", "0", "8", "2", "45", "14"], ["basel", "total", "", "66", "22", "7", "0", "11", "2", "84", "24"], ["southampton", "2018-19", "premier league", "15", "0", "3", "0", "-", "-", "18", "0"], ["southampton", "total", "", "15", "0", "3", "0", "0", "0", "18", "0"], ["career total", "", "", "205", "65", "21", "6", "28", "9", "254", "80"]]}, "native_split": "train", "question": "which club did mohamed elyounoussi play for in 2011?", "source_prompt": "{table}\n{question}", "answer_dp": "sarpsborg 08", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_177_totto20947-0_0_1", "source_table_id": "177_totto20947-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2734_14829_1", "source": "hitab", "table_key": "a7f666c928430ef5a1323fe951a471bd1a36069f", "grid": {"TD": 3, "LD": 3, "nrows": 14, "ncols": 13, "section_rows": [4, 7, 8, 11], "cells": [["", "", "", "number", "moderate-to-vigorous physical activity", "moderate-to-vigorous physical activity", "moderate-to-vigorous physical activity", "screen time", "screen time", "screen time", "sleep", "sleep", "sleep"], ["", "", "", "", "average minutes", "95% confidence interval", "95% confidence interval", "average hours", "95% confidence interval", "95% confidence interval", "average hours", "95% confidence interval", "95% confidence interval"], ["", "", "", "", "", "from", "to", "", "from", "to", "", "from", "to"], ["total", "", "", "3111", "53.8", "50.6", "57", "3.1", "2.9", "3.2", "9", "8.9", "9.1"], ["sex", "", "", "", "", "", "", "", "", "", "", "", ""], ["sex", "boys", "", "1553", "60.1", "55.8", "64.4", "3.3", "3.1", "3.5", "9", "8.9", "9.1"], ["sex", "girls", "", "1558", "47.1", "44.6", "49.7", "2.8", "2.6", "3", "9", "8.9", "9.1"], ["age group", "", "", "", "", "", "", "", "", "", "", "", ""], ["age group", "5 to 11", "", "1985", "61.3", "57.8", "64.7", "2.3", "2.2", "2.5", "9.6", "9.5", "9.7"], ["age group", "5 to 11", "boys", "980", "68.3", "63.3", "73.2", "2.4", "2.2", "2.5", "9.6", "9.5", "9.7"], ["age group", "5 to 11", "girls", "1005", "54", "51.1", "56.8", "2.3", "2.1", "2.5", "9.7", "9.6", "9.8"], ["age group", "12 to 17", "", "1126", "46.3", "42.5", "50.1", "3.8", "3.5", "4", "8.3", "8.2", "8.5"], ["age group", "12 to 17", "boys", "573", "52", "46.9", "57.1", "4.2", "3.8", "4.5", "8.4", "8.2", "8.6"], ["age group", "12 to 17", "girls", "553", "40.3", "36.9", "43.6", "3.4", "3", "3.7", "8.2", "8.1", "8.4"]]}, "native_split": "test", "question": "which age group had more averaged mvpa,5-11 or 12-17?", "source_prompt": "{table}\n{question}", "answer_dp": "5 to 11", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2734_14829_1", "source_table_id": "2734"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1057_6080_1", "source": "hitab", "table_key": "30b4a3f06e5a7862b9c98b842826880025413703", "grid": {"TD": 3, "LD": 2, "nrows": 9, "ncols": 17, "section_rows": [6], "cells": [["", "", "heterosexual", "heterosexual", "heterosexual", "gay or lesbian", "gay or lesbian", "gay or lesbian", "bisexual", "bisexual", "bisexual", "sexual orientation n.e.c", "sexual orientation n.e.c", "sexual orientation n.e.c", "total sexual minority", "total sexual minority", "total sexual minority"], ["", "", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval", "percent", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to", "", "from", "to", "", "from", "to", "", "from", "to", "", "from", "to"], ["binge drinking in the past 12 months", "", "44.1", "43.4", "44.8", "52.7", "46.6", "58.7", "55.3", "48.4", "61.9", "38.3", "23.8", "55.3", "53", "48.5", "57.4"], ["non-medicinal cannabis use in the past 12 months", "", "15.3", "14.8", "15.9", "28.5", "23.6", "33.9", "46.3", "39.8", "53", "20.8", "11.1", "35.6", "37", "32.8", "41.3"], ["non-prescribed drug use in the past 12 months", "", "2.6", "2.3", "2.9", "6.6", "4.2", "10.2", "14", "10.3", "18.8", "", "", "", "10.3", "8", "13"], ["used drugs or alcohol to cope", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["used drugs or alcohol to cope", "with abuse or violence that occurred in lifetime", "10.4", "9.9", "11", "18.2", "14.1", "23.2", "29.5", "23.8", "35.9", "20.2", "11", "34.3", "24.1", "20.4", "28.3"], ["used drugs or alcohol to cope", "with abuse or violence that occurred in past 12 months", "9.7", "7.2", "13", "", "", "", "31", "18.2", "47.4", "", "", "", "29.4", "19.1", "42.3"]]}, "native_split": "test", "question": "which group of people is least likely to have engaged in health risk behaviours such as binge drinking and drug use in the 12 months preceding the sspps?", "source_prompt": "{table}\n{question}", "answer_dp": "heterosexual", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1057_6080_1", "source_table_id": "1057"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2335_12532_1", "source": "hitab", "table_key": "2c20a233b56065c72081b775dea013494a1c7d99", "grid": {"TD": 2, "LD": 2, "nrows": 15, "ncols": 8, "section_rows": [], "cells": [["", "", "2015", "2015", "2016", "2016", "2017", "2017"], ["", "", "previously published", "revised growth", "previously published", "revised growth", "previously published", "revised growth"], ["percentage", "newfoundland and labrador", "-1.5", "-1.08", "1.72", "1.72", "2.1", "0.88"], ["percentage", "prince edward island", "1.51", "1.4", "2.19", "1.68", "3.17", "3.45"], ["percentage", "nova scotia", "1.16", "0.76", "1.05", "1.52", "1.22", "1.49"], ["percentage", "new brunswick", "2.3", "0.79", "1.2", "1.39", "1.95", "1.99"], ["percentage", "quebec", "1.01", "0.96", "1.53", "1.46", "3.08", "2.9"], ["percentage", "ontario", "2.78", "2.65", "2.62", "2.45", "2.79", "2.77"], ["percentage", "manitoba", "1.36", "1.37", "2.12", "1.67", "2.91", "3.13"], ["percentage", "saskatchewan", "-1.24", "-0.76", "-0.45", "-0.26", "2.86", "2.29"], ["percentage", "alberta", "-3.93", "-3.49", "-3.6", "-4.06", "4.85", "4.59"], ["percentage", "british columbia", "3.25", "2.32", "3.59", "3.36", "3.94", "4.01"], ["percentage", "yukon", "-7.94", "-7.6", "8.29", "7", "-1.44", "2.9"], ["percentage", "northwest territories", "1.24", "1.02", "0.83", "1.27", "5.25", "3.88"], ["percentage", "nunavut", "-0.66", "-0.41", "1.9", "3.46", "13.33", "10.31"]]}, "native_split": "test", "question": "among all the provinces, which province has the largest revision between the previously-published rate and the revised growth rate in 2015?", "source_prompt": "{table}\n{question}", "answer_dp": "new brunswick", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2335_12532_1", "source_table_id": "2335"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_448_2446_2", "source": "hitab", "table_key": "066a69ff7033ddf783b5228575b90e8f23f2373e", "grid": {"TD": 2, "LD": 2, "nrows": 11, "ncols": 10, "section_rows": [2, 7], "cells": [["", "", "sexual attack", "sexual attack", "unwanted sexual touching", "unwanted sexual touching", "sexual activity where unable to consent", "sexual activity where unable to consent", "total sexual assault", "total sexual assault"], ["", "", "number", "percent", "number", "percent", "number", "percent", "number", "percent"], ["2018", "", "", "", "", "", "", "", "", ""], ["2018", "female victims", "60", "0.8", "300", "3.7", "70", "0.9", "340", "4.3"], ["2018", "male victims", "60", "0.1", "480", "1", "50", "0.1", "520", "1.1"], ["2018", "gender diverse victims", "", "", "", "", "", "", "", ""], ["2018", "total victims", "140", "0.2", "800", "1.4", "130", "0.2", "900", "1.6"], ["2016", "", "", "", "", "", "", "", "", ""], ["2016", "female victims", "70", "0.9", "320", "4", "60", "0.7", "380", "4.8"], ["2016", "male victims", "80", "0.2", "520", "1.1", "60", "0.1", "570", "1.2"], ["2016", "total victims", "150", "0.3", "840", "1.5", "110", "0.2", "960", "1.7"]]}, "native_split": "test", "question": "in 2018, which is comparatively less common, sexual activity or unwanted sexual touching?", "source_prompt": "{table}\n{question}", "answer_dp": "sexual activity where unable to consent", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_448_2446_2", "source_table_id": "448"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_587_totto67450-0_0_1", "source": "hitab", "table_key": "5f8da1d444b0c43ff568ceb6bea3990a794b8f1f", "grid": {"TD": 2, "LD": 2, "nrows": 9, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "fa cup", "fa cup", "league cup", "league cup", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["wycombe wanderers", "2015-16", "league two", "15", "0", "0", "0", "1", "0", "1", "0", "17", "0"], ["wingate & finchley", "2016-17", "isthmian league premier division", "31", "0", "2", "0", "-", "-", "6", "0", "39", "0"], ["wealdstone", "2016-17", "national league south", "11", "0", "-", "-", "11", "0", "none", "none", "none", "none"], ["wealdstone", "2017-18", "national league south", "25", "3", "1", "0", "-", "-", "3", "0", "29", "3"], ["wealdstone", "total", "", "36", "3", "1", "0", "-", "-", "3", "0", "40", "3"], ["halifax town", "2018-19", "national league", "0", "0", "0", "0", "-", "-", "0", "0", "0", "0"], ["career total", "", "", "82", "3", "3", "0", "1", "0", "10", "0", "96", "3"]]}, "native_split": "test", "question": "in 2016-17, which league did sellers join side wealdstone?", "source_prompt": "{table}\n{question}", "answer_dp": "national league south", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_587_totto67450-0_0_1", "source_table_id": "587_totto67450-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2627_14234_1", "source": "hitab", "table_key": "c2ecccb7a0633ca6c89a7251c69ade51c6bbfa4e", "grid": {"TD": 1, "LD": 3, "nrows": 50, "ncols": 10, "section_rows": [1, 4, 12, 15, 18, 21, 24, 27, 30, 35, 38], "cells": [["", "", "", "police", "school system", "banks", "justice system and courts", "media", "federal parliament", "major corporations"], ["predicted probabilities", "sex", "", "", "", "", "", "", "", ""], ["predicted probabilities", "sex", "male", "0.74", "0.6", "0.56", "0.56", "0.4", "0.35", "0.28"], ["predicted probabilities", "sex", "female", "0.79", "0.62", "0.62", "0.58", "0.4", "0.38", "0.31"], ["predicted probabilities", "age group", "", "", "", "", "", "", "", ""], ["predicted probabilities", "age group", "15 to 24 years", "0.76", "0.63", "0.67", "0.63", "0.32", "0.49", "0.31"], ["predicted probabilities", "age group", "25 to 34 years", "0.72", "0.6", "0.5", "0.52", "0.36", "0.34", "0.24"], ["predicted probabilities", "age group", "35 to 44 years", "0.77", "0.6", "0.52", "0.56", "0.4", "0.34", "0.27"], ["predicted probabilities", "age group", "45 to 54 years", "0.75", "0.57", "0.56", "0.57", "0.41", "0.34", "0.27"], ["predicted probabilities", "age group", "55 to 64 years", "0.76", "0.6", "0.58", "0.56", "0.43", "0.34", "0.3"], ["predicted probabilities", "age group", "65 to 74 years", "0.79", "0.64", "0.67", "0.58", "0.45", "0.37", "0.34"], ["predicted probabilities", "age group", "75 years and older", "0.85", "0.74", "0.79", "0.65", "0.56", "0.42", "0.45"], ["predicted probabilities", "marital status", "", "", "", "", "", "", "", ""], ["predicted probabilities", "marital status", "married/common law", "0.78", "0.62", "0.59", "0.59", "0.4", "0.38", "0.3"], ["predicted probabilities", "marital status", "not married or in a common-law relationship", "0.73", "0.6", "0.58", "0.55", "0.41", "0.35", "0.28"], ["predicted probabilities", "level of education", "", "", "", "", "", "", "", ""], ["predicted probabilities", "level of 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"", "", "", "", "", ""], ["predicted probabilities", "visible minority", "visible minority", "0.71", "0.66", "0.65", "0.6", "0.44", "0.46", "0.36"], ["predicted probabilities", "visible minority", "non-visible minority", "0.77", "0.6", "0.58", "0.57", "0.39", "0.35", "0.28"], ["predicted probabilities", "aboriginal identity", "", "", "", "", "", "", "", ""], ["predicted probabilities", "aboriginal identity", "aboriginal", "0.72", "0.58", "0.59", "0.52", "0.37", "0.35", "0.27"], ["predicted probabilities", "aboriginal identity", "non-aboriginal", "0.76", "0.61", "0.59", "0.57", "0.4", "0.37", "0.29"], ["predicted probabilities", "immigrant status", "", "", "", "", "", "", "", ""], ["predicted probabilities", "immigrant status", "non-immigrant", "0.75", "0.6", "0.58", "0.55", "0.4", "0.35", "0.29"], ["predicted probabilities", "immigrant status", "longer-term immigrant", "0.76", "0.64", "0.58", "0.63", "0.4", "0.41", "0.27"], ["predicted probabilities", "immigrant status", "recent immigrant", "0.83", "0.69", "0.64", "0.7", "0.47", "0.51", "0.37"], ["predicted probabilities", "immigrant status", "immigrant, length of time in canada unknown", "0.79", "0.64", "0.62", "0.62", "0.37", "0.38", "0.37"], ["predicted probabilities", "mother tongue", "", "", "", "", "", "", "", ""], ["predicted probabilities", "mother tongue", "official language", "0.75", "0.6", "0.58", "0.56", "0.39", "0.35", "0.28"], ["predicted probabilities", "mother tongue", "non-official language only", "0.79", "0.66", "0.64", "0.65", "0.46", "0.43", "0.34"], ["predicted probabilities", "province of residence", "", "", "", "", "", "", "", ""], ["predicted probabilities", "province of residence", "newfoundland and labrador", "0.84", "0.73", "0.67", "0.62", "0.51", "0.4", "0.41"], ["predicted probabilities", "province of residence", "prince edward island", "0.74", "0.67", "0.58", "0.63", "0.42", "0.28", "0.34"], ["predicted probabilities", "province of residence", "nova scotia", "0.78", "0.59", "0.63", "0.6", "0.44", "0.31", "0.29"], ["predicted probabilities", "province of residence", "new brunswick", "0.8", "0.69", "0.63", "0.65", "0.49", "0.35", "0.35"], ["predicted probabilities", "province of residence", "quebec", "0.74", "0.61", "0.54", "0.54", "0.43", "0.39", "0.27"], ["predicted probabilities", "province of residence", "ontario", "0.77", "0.61", "0.6", "0.62", "0.39", "0.37", "0.29"], ["predicted probabilities", "province of residence", "manitoba", "0.78", "0.65", "0.63", "0.51", "0.4", "0.37", "0.36"], ["predicted probabilities", "province of residence", "saskatchewan", "0.79", "0.66", "0.64", "0.61", "0.43", "0.38", "0.33"], ["predicted probabilities", "province of residence", "alberta", "0.78", "0.63", "0.61", "0.57", "0.36", "0.38", "0.3"], ["predicted probabilities", "province of residence", "british columbia", "0.73", "0.57", "0.57", "0.49", "0.38", "0.32", "0.28"], ["predicted probabilities", "total", "", "0.76", "0.61", "0.59", "0.57", "0.4", "0.37", "0.29"]]}, "native_split": "test", "question": "which group of people had a higher probability of confidence in justice system and courts? non-aboriginal people or aboriginal people?", "source_prompt": "{table}\n{question}", "answer_dp": "non-aboriginal", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2627_14234_1", "source_table_id": "2627"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_365_totto41840-3_0_1", "source": "hitab", "table_key": "5a3de954885d81458776c3a32d007cf7feaa1d51", "grid": {"TD": 2, "LD": 2, "nrows": 15, "ncols": 11, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["gremio", "2013", "serie a", "0", "0", "0", "0", "4", "0", "4", "0"], ["oeste", "2012", "paulista a1", "0", "0", "0", "0", "2", "0", "2", "0"], ["red bull brasil", "2014", "serie d", "0", "0", "0", "0", "8", "1", "8", "1"], ["atenas", "2014-15", "primera division", "26", "1", "0", "0", "-", "-", "26", "1"], ["red bull brasil", "2015", "serie d", "8", "0", "0", "0", "0", "0", "8", "0"], ["red bull brasil", "2016", "serie d", "0", "0", "1", "0", "2", "0", "3", "0"], ["red bull brasil", "total", "", "8", "0", "1", "0", "2", "0", "11", "0"], ["mumbai city", "2016", "indian super league", "14", "1", "-", "-", "14", "1", "none", "none"], ["atletico tubarao", "2017", "catarinense a1", "-", "-", "0", "0", "14", "1", "14", "1"], ["mumbai city", "2017-18", "indian super league", "17", "0", "0", "0", "-", "-", "17", "0"], ["renofa yamaguchi", "2018", "j2 league", "4", "0", "0", "0", "-", "-", "4", "0"], ["atk", "2018-19", "indian super league", "4", "0", "0", "0", "-", "-", "4", "0"], ["career total", "", "", "73", "2", "1", "0", "30", "2", "104", "4"]]}, "native_split": "test", "question": "in 2016, which league did gerson vieira appear for mumbai city in indian super league?", "source_prompt": "{table}\n{question}", "answer_dp": "indian super league", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_365_totto41840-3_0_1", "source_table_id": "365_totto41840-3"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_145_totto17495-4_0_1", "source": "hitab", "table_key": "f2d7779a77410dab338c86ac1f949c0df8571c4b", "grid": {"TD": 2, "LD": 2, "nrows": 7, "ncols": 13, "section_rows": [], "cells": [["", "", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration", "penetration"], ["", "", "100 m", "250 m", "500 m", "750 m", "1000 m", "1250 m", "1500 m", "1750 m", "2000 m", "2500 m", "3000 m"], ["m62 versus fha", "792 m/s", "124", "123", "122", "119", "116", "113", "110", "105", "101", "92", "83"], ["m62 versus rha", "792 m/s", "125", "121", "116", "111", "106", "101", "97", "93", "89", "81", "74"], ["m79 versus fha", "792 m/s", "132", "124", "112", "101", "92", "83", "75", "68", "62", "50", "41"], ["m79 versus rha", "792 m/s", "154", "145", "131", "119", "107", "97", "88", "79", "72", "59", "48"], ["m93", "1,036 m/s", "239", "227", "208", "191", "175", "160", "147", "135", "124", "108", "88"]]}, "native_split": "test", "question": "what was the muzzle velocity of m93?", "source_prompt": "{table}\n{question}", "answer_dp": "1,036 m/s", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_145_totto17495-4_0_1", "source_table_id": "145_totto17495-4"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_335_1766_1", "source": "hitab", "table_key": "511dd0ff1c925589317711e5a3384ccc5760f1aa", "grid": {"TD": 2, "LD": 2, "nrows": 5, "ncols": 10, "section_rows": [2], "cells": [["", "", "sexual assault", "sexual assault", "robbery", "robbery", "physical assault", "physical assault", "total violent victimization", "total violent victimization"], ["", "", "number", "rate per 1,000", "number", "rate per 1,000", "number", "rate per 1,000", "number", "rate per 1,000"], ["sex", "", "", "", "", "", "", "", "", ""], ["sex", "male", "80", "5", "113", "8", "786", "54", "979", "67"], ["sex", "female", "553", "37", "77", "5", "636", "43", "1266", "85"]]}, "native_split": "test", "question": "what contributed the fewest violent victimization incidents among women?", "source_prompt": "{table}\n{question}", "answer_dp": "robbery", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_335_1766_1", "source_table_id": "335"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_104_totto12294-0_0_1", "source": "hitab", "table_key": "8b2401839c0d05ff362026ec411147071d49ae08", "grid": {"TD": 2, "LD": 3, "nrows": 28, "ncols": 11, "section_rows": [], "cells": [["", "", "", "league", "league", "fa cup", "fa cup", "other", "other", "total", "total"], ["", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["wigan athletic", "1982-83", "third division", "5", "1", "0", "0", "0", "0", "5", "1"], ["rotherham united", "1982-83", "second division", "4", "0", "0", "0", "0", "0", "4", "0"], ["everton", "1980-81", "first division", "11", "0", "0", "0", "0", "0", "11", "0"], ["everton", "1981-82", "first division", "13", "0", "1", "0", "2", "0", "16", "0"], ["everton", "1982-83", "first division", "0", "0", "0", "0", "0", "0", "0", "0"], ["everton", "total", "", "24", "0", "1", "0", "2", "0", "27", "0"], ["preston north end", "1982-83", "third division", "19", "0", "0", "0", "0", "0", "19", "0"], ["preston north end", "1983-84", "third division", "19", "0", "1", "0", "5", "0", "25", "0"], ["preston north end", "total", "", "38", "0", "1", "0", "5", "0", "44", "0"], ["port vale", "1984-85", "fourth division", "3", "0", "0", "0", "0", "0", "3", "0"], ["bolton wanderers", "1984-85", "third division", "4", "0", "0", "0", "1", "0", "5", "0"], ["stockport county", "1984-85", "fourth division", "12", "2", "0", "0", "0", "0", "12", "2"], ["stockport county", "1985-86", "fourth division", "1", "0", "0", "0", "1", "0", "2", "0"], ["stockport county", "total", "", "13", "2", "0", "0", "1", "0", "14", "2"], ["southport", "1985-86", "northern premier league", "11", "1", "0", "0", "2", "0", "13", "1"], ["southport", "1986-87", "northern premier league", "37", "4", "9", "1", "17", "0", "63", "5"], ["southport", "1987-88", "northern premier league", "21", "0", "1", "0", "4", "0", "26", "0"], ["southport", "total", "", "69", "5", "10", "1", "23", "0", "102", "6"], ["macclesfield town", "1987-88", "conference", "12", "0", "0", "0", "3", "1", "15", "1"], ["macclesfield town", "1988-89", "conference", "2", "0", "0", "0", "0", "0", "2", "0"], ["macclesfield town", "total", "", "14", "0", "0", "0", "3", "1", "17", "1"], ["southport", "1993-94", "conference", "26", "1", "0", "0", "10", "0", "36", "1"], ["southport", "1994-95", "conference", "38", "1", "2", "0", "6", "0", "46", "1"], ["southport", "1995-96", "conference", "36", "1", "1", "0", "10", "0", "47", "1"], ["southport", "total", "", "100", "3", "3", "0", "26", "0", "129", "3"], ["career total", "", "", "274", "11", "15", "1", "61", "1", "350", "13"]]}, "native_split": "test", "question": "which club did lodge play for in 1985-86?", "source_prompt": "{table}\n{question}", "answer_dp": "stockport county", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_104_totto12294-0_0_1", "source_table_id": "104_totto12294-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1569_8798_1", "source": "hitab", "table_key": "461d2e5c59e54115855d6b8358f486b26b6532c7", "grid": {"TD": 2, "LD": 3, "nrows": 7, "ncols": 7, "section_rows": [2], "cells": [["", "", "", "women", "women", "men", "men"], ["", "", "", "non-aboriginal", "aboriginal", "non-aboriginal", "aboriginal"], ["percent", "15 or older", "", "14.7", "22", "12.5", "14.6"], ["percent", "15 or older", "15 to 24", "4.1", "8.1", "4.4", "7"], ["percent", "15 or older", "25 to 54", "9.1", "21.3", "8.1", "12.1"], ["percent", "15 or older", "55 to 64", "20.8", "33.9", "19", "26.8"], ["percent", "15 or older", "65 or older", "35", "53.4", "30.6", "39.2"]]}, "native_split": "test", "question": "among those aged 15 to 24, who were more likely to have disabilities,aboriginal women or non-aboriginal women?", "source_prompt": "{table}\n{question}", "answer_dp": "aboriginal", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1569_8798_1", "source_table_id": "1569"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1510_8388_1", "source": "hitab", "table_key": "95528a53b8aa2decfbee9352d1e21edb623b0ef3", "grid": {"TD": 1, "LD": 2, "nrows": 18, "ncols": 5, "section_rows": [], "cells": [["", "", "women", "men", "total"], ["percent", "crime was minor and not worth taking the time to report", "71", "75", "71"], ["percent", "incident was a private or personal matter and it was handled informally", "68", "62", "67"], ["percent", "no one was harmed", "66", "42", "63"], ["percent", "did not want the hassle of dealing with the police", "47", "33", "45"], ["percent", "police would not have considered incident important enough", "43", "43", "43"], ["percent", "lack of evidence", "46", "18", "43"], ["percent", "offender would not be convicted or adequately punished", "41", "27", "40"], ["percent", "no harm was intended", "38", "49", "39"], ["percent", "feared or did not want the hassle of dealing with the court process", "34", "37", "34"], ["percent", "did not want the offender in trouble with the law", "29", "36", "30"], ["percent", "did not want others to find out about the victimization", "32", "", "30"], ["percent", "police would not have been efficient or effective", "27", "", "26"], ["percent", "fear of revenge by the offender or others", "24", "", "22"], ["percent", "police would not have been able to find or identify the offender", "22", "", "21"], ["percent", "police would be biased", "13", "", "13"], ["percent", "received unsatisfactory service from the police in the past", "13", "", "13"], ["percent", "afraid reporting would bring shame and dishonour to the family", "13", "", "12"]]}, "native_split": "test", "question": "which sex of people were more likely to state that they did not report to the police because no one was harmed during the incident?", "source_prompt": "{table}\n{question}", "answer_dp": "women", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1510_8388_1", "source_table_id": "1510"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2701_14609_1", "source": "hitab", "table_key": "60fb8e84a913d349acf55b312b3476172bf08aa4", "grid": {"TD": 1, "LD": 3, "nrows": 54, "ncols": 7, "section_rows": [3, 6, 12, 15, 20, 24, 27, 33, 38, 41, 47, 51], "cells": [["", "", "", "neither cyberbullied nor cyberstalked", "cyberbullied, but not cyberstalked", "cyberstalked, but not cyberbullied", "cyberbullied and cyberstalked"], ["percent", "all 15-to-29 year-olds", "", "83.2", "6", "5.6", "5.2"], ["percent", "sociodemographic", "", "", "", "", ""], ["percent", "sexual orientation", "", "", "", "", ""], ["percent", "sexual orientation", "heterosexual", "84.6", "5", "5.6", "4.8"], ["percent", "sexual orientation", "homosexual/bi-sexual", "66", "12.2", "9.6", "12.3"], ["percent", "age group", "", "", "", "", ""], ["percent", "age group", "15 to 17", "81", "9.3", "5", "4.8"], ["percent", "age group", "18 to 20", "80.5", "7.8", "5.6", "6.1"], ["percent", "age group", "21 to 23", "84", "5.1", "6.1", "4.8"], ["percent", "age group", "24 to 26", "84.5", "5", "5.7", "4.8"], ["percent", "age group", "27 to 29", "85.4", "3.5", "5.6", "5.5"], ["percent", "gender", "", "", "", "", ""], ["percent", "gender", "men", "85.2", "6.1", "4.7", "4.1"], ["percent", "gender", "women", "81.1", "6", "6.6", "6.4"], ["percent", "province/region of residence", "", "", "", "", ""], ["percent", "province/region of residence", "atlantic", "82.1", "6.8", "5.2", "5.9"], ["percent", "province/region of residence", "quebec", "86.6", "5.9", "4.1", "3.4"], ["percent", "province/region of residence", "ontario", "81", "7", "6", "6.1"], ["percent", "province/region of residence", "west", "83.7", "4.9", "6.2", "5.2"], ["percent", "population size of community", "", "", "", "", ""], ["percent", "population size of community", "rural", "84", "6.6", "3.7", "5.8"], ["percent", "population size of community", "population centre", "82.9", "5.9", "6.1", "5.1"], ["percent", "family, family background", "", "", "", "", ""], ["percent", "marital status", "", "", "", "", ""], ["percent", "marital status", "currently married or ever-married/common-law", "88.8", "4", "4.1", "3.2"], ["percent", "marital status", "single/never-married", "81.7", "6.6", "6", "5.7"], ["percent", "parental education", "", "", "", "", ""], ["percent", "parental education", "both have high school or less", "81.1", "6.7", "7.1", "5.1"], ["percent", "parental education", "at least one has postsecondary education", "84.6", "5.3", "5", "5"], ["percent", "parental education", "both have postsecondary education", "82.5", "6.2", "5.8", "5.5"], ["percent", "parental education", "do not know either parent's education level", "84.4", "6", "4.5", "5.1"], ["percent", "education/main activity/household income", "", "", "", "", ""], ["percent", "highest level of education", "", "", "", "", ""], ["percent", "highest level of education", "less than high school", "81.2", "9.1", "4.5", "5.3"], ["percent", "highest level of education", "high school", "81.3", "5.7", "7", "6"], ["percent", "highest level of education", "postsecondary - non-university", "86.7", "4.4", "5", "4"], ["percent", "highest level of education", "postsecondary - university", "84.3", "5.2", "5.2", "5.3"], ["percent", "main activity in the last year", "", "", "", "", ""], ["percent", "main activity in the last year", "non-student", "84.5", "4.9", "5.6", "4.9"], ["percent", "main activity in the last year", "student", "81.7", "7.2", "5.6", "5.5"], ["percent", "household income", "", "", "", "", ""], ["percent", "household income", "less than $40,000", "75.6", "6.3", "11.1", "7.1"], ["percent", "household income", "$40,000 to $79,999", "83.4", "7.1", "5.2", "4.3"], ["percent", "household income", "$80,000 or more", "85.3", "5", "5.1", "4.6"], ["percent", "household income", "not stated", "82.6", "6.3", "5.4", "5.6"], ["percent", "cultural/ethnic", "", "", "", "", ""], ["percent", "religious affiliation", "", "", "", "", ""], ["percent", "religious affiliation", "none", "80.6", "6.2", "6", "7.2"], ["percent", "religious affiliation", "christian", "84.4", "6.3", "5.2", "4.2"], ["percent", "religious affiliation", "non-christian", "83.8", "4.2", "7.4", "4.6"], ["percent", "visible minority", "", "", "", "", ""], ["percent", "visible minority", "no", "82.2", "6.5", "5.6", "5.8"], ["percent", "visible minority", "yes", "86.1", "4.8", "5.4", "3.7"]]}, "native_split": "test", "question": "who experienced online victimization more, homosexual and bisexual population or those heterosexual?", "source_prompt": "{table}\n{question}", "answer_dp": "homosexual/bi-sexual", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2701_14609_1", "source_table_id": "2701"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2708_14691_1", "source": "hitab", "table_key": "ba28c5606de82e607e824b6d7dc8a7e22b3d3569", "grid": {"TD": 2, "LD": 2, "nrows": 32, "ncols": 5, "section_rows": [3, 8, 11, 15, 18, 21, 25, 28], "cells": [["", "", "geometric mean", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to"], ["total", "", "1.3", "1.1", "1.4"], ["age group", "", "", "", ""], ["age group", "6 to 8", "1.8", "1.6", "2.1"], ["age group", "9 to 11", "1.4", "1.2", "1.7"], ["age group", "12 to 14", "1.2", "1", "1.4"], ["age group", "15 to 17", "0.9", "0.8", "1.1"], ["sex", "", "", "", ""], ["sex", "male", "1.2", "1.1", "1.4"], ["sex", "female", "1.3", "1.2", "1.5"], ["time of urine collection", "", "", "", ""], ["time of urine collection", "morning", "1.1", "1", "1.3"], ["time of urine collection", "afternoon", "1.5", "1.3", "1.7"], ["time of urine collection", "evening", "1.4", "1", "1.7"], ["ethnicity", "", "", "", ""], ["ethnicity", "white", "1.3", "1.2", "1.5"], ["ethnicity", "non-white", "1.3", "1", "1.6"], ["exposure to second-hand smoke", "", "", "", ""], ["exposure to second-hand smoke", "once a week or less", "1.2", "1.1", "1.4"], ["exposure to second-hand smoke", "every day or almost every day", "1.6", "1.4", "2"], ["household income", "", "", "", ""], ["household income", "low/lower-middle", "1.7", "1.4", "2"], ["household income", "upper-middle", "1.2", "1", "1.4"], ["household income", "highest", "1.2", "1.1", "1.4"], ["household education", "", "", "", ""], ["household education", "secondary school graduation or less", "1.3", "1", "1.6"], ["household education", "at least some postsecondary", "1.3", "1.1", "1.5"], ["body mass index", "", "", "", ""], ["body mass index", "neither overweight nor obese", "1.3", "1.2", "1.5"], ["body mass index", "overweight", "1.3", "1", "1.7"], ["body mass index", "obese", "1.2", "1", "1.4"]]}, "native_split": "test", "question": "list the household income with the highest bpa concentrations.", "source_prompt": "{table}\n{question}", "answer_dp": "low/lower-middle", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2708_14691_1", "source_table_id": "2708"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_400_totto46213-0_0_1", "source": "hitab", "table_key": "65a1e3ae82a4e19f3155f52817e359ea95750a46", "grid": {"TD": 2, "LD": 2, "nrows": 13, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "continental", "continental", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["independiente", "1997-98", "argentine primera division", "5", "0", "0", "0", "-", "-", "0", "0", "5", "0"], ["toros neza", "2000-01", "primera division a", "15", "1", "0", "0", "-", "-", "0", "0", "15", "1"], ["america de cali", "2001", "categoria primera a", "3", "0", "0", "0", "0", "0", "0", "0", "3", "0"], ["argentinos juniors", "2001-02", "argentine primera division", "33", "1", "0", "0", "-", "-", "0", "0", "33", "1"], ["nueva chicago", "2002-03", "argentine primera division", "34", "1", "0", "0", "-", "-", "0", "0", "34", "1"], ["nueva chicago", "2005-06", "primera b nacional", "9", "0", "0", "0", "-", "-", "0", "0", "9", "0"], ["nueva chicago", "total", "", "43", "1", "0", "0", "-", "-", "0", "0", "43", "1"], ["jorge wilstermann", "2006", "bolivian primera division", "8", "0", "0", "0", "-", "-", "0", "0", "8", "0"], ["alumni", "2007-08", "torneo argentino a", "22", "1", "0", "0", "-", "-", "0", "0", "22", "1"], ["tiro federal", "2008-09", "torneo argentino b", "15", "1", "0", "0", "-", "-", "0", "0", "15", "1"], ["career total", "", "", "144", "5", "0", "0", "0", "0", "0", "0", "144", "5"]]}, "native_split": "train", "question": "which league did garcia play side jorge wilstermann in 2006?", "source_prompt": "{table}\n{question}", "answer_dp": "bolivian primera division", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_400_totto46213-0_0_1", "source_table_id": "400_totto46213-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_246_1342_1(3)", "source": "hitab", "table_key": "20dd42f0f057fe47b8589336019cfd48f78366fd", "grid": {"TD": 3, "LD": 1, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["", "household", "household", "census family", "census family", "census family"], ["", "average number of people per household", "proportion of households with only one person", "average number of children aged 0 to 14 among couples with children", "proportion of couples with children in the family", "proportion of persons living outside a census family"], ["", "number", "percent", "number", "percent", "percent"], ["syria", "4.8", "3.6", "2.8", "85.4", "4.2"], ["iraq", "4", "13.4", "2", "74.1", "9.6"], ["afghanistan", "5.4", "3", "2.4", "71.8", "3.5"], ["eritrea", "3.5", "9.8", "2.2", "62", "25.3"], ["democratic republic of the congo", "4.4", "14.3", "3", "46.5", "25.7"], ["other countries", "3.7", "13.4", "2.2", "58.4", "21.6"], ["total, excluding syria", "4", "12", "2.2", "63.2", "18"], ["total", "4.4", "7.1", "2.6", "77.2", "9.5"]]}, "native_split": "test", "question": "which source country had the largest proportion of couples with chilren and a relatively high number of children in these families among all the source countreis?", "source_prompt": "{table}\n{question}", "answer_dp": "syria", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_246_1342_1(3)", "source_table_id": "246"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_501_2750_4", "source": "hitab", "table_key": "989106c32636b122669e993871761ef9a4591ff8", "grid": {"TD": 1, "LD": 3, "nrows": 41, "ncols": 17, "section_rows": [1, 9, 17, 25, 33], "cells": [["", "", "", "n.l", "p.e.i", "n.s", "n.b", "que", "ont", "man", "sask", "alta", "b.c", "y.t", "n.w.t", "nvt", "canada"], ["for thousand women", "2012", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["for thousand women", "2012", "15 to 19 years", "14.6", "13.4", "17.4", "20", "8.9", "9.3", "27.1", "33", "15.9", "7.9", "10.9", "29.8", "97.8", "11.9"], ["for thousand women", "2012", "20 to 24 years", "49.9", "45.1", "52.4", "67", "43.8", "35.8", "73.2", "77", "57.4", "33.3", "63.9", "85.7", "185.7", "44.1"], ["for thousand women", "2012", "25 to 29 years", "85.2", "103.1", "92.8", "103.4", "111.7", "85.9", "114.2", "122.6", "105.3", "76.8", "91.3", "105.2", "129.1", "95.7"], ["for thousand women", "2012", "30 to 34 years", "85", "93.7", "90.7", "86.8", "109.4", "109.9", "111.2", "113.6", "110.2", "99.3", "101.6", "99.1", "93.6", "107.3"], ["for thousand women", "2012", "35 to 39 years", "34", "40.3", "40", "31.5", "50.6", "57.2", "50.2", "46.7", "55.1", "56.3", "58.5", "49.8", "49.9", "53.4"], ["for thousand women", "2012", "40 to 44 years", "5.1", "6", "7", "4.8", "10", "11.7", "9.8", "8.4", "11", "11.7", "12.6", "17.9", "10.5", "10.7"], ["for thousand women", "2012", "45 to 49 years", "0.3", "0.3", "0.2", "0.3", "0.4", "0.6", "0.4", "0.4", "0.5", "0.6", "0.7", "1.2", "0", "0.5"], ["for thousand women", "2013", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["for thousand women", "2013", "15 to 19 years", "16.1", "12", "16.6", "16", "8.1", "8.4", "25.3", "29", "15.1", "7.5", "17.8", "26.3", "124.3", "11"], ["for thousand women", "2013", "20 to 24 years", "50.5", "47.3", "50.5", "61.5", "42.8", "33.3", "68.8", "73.2", "54.9", "31.3", "48.9", "82.6", "192.4", "41.9"], ["for thousand women", "2013", "25 to 29 years", "88.1", "109", "83.9", "106.1", "108.5", "82.8", "114.1", "120.1", "102.2", "74.7", "86", "95.8", "130.7", "93"], ["for thousand women", "2013", "30 to 34 years", "90", "107.7", "91.7", "93", "109.8", "109.4", "111.4", "110", "109.5", "98.7", "94.5", "109.8", "94.5", "107.2"], ["for thousand women", "2013", "35 to 39 years", "34.8", "42", "41.1", "32.4", "51.6", "56.6", "52.9", "48.5", "54.5", "57", "53.1", "53.1", "58.9", "53.6"], ["for thousand women", "2013", "40 to 44 years", "5.5", "6.5", "7", "5", "10.5", "11.4", "9.8", "9.3", "10.9", "12.1", "8.6", "6.6", "5.8", "10.7"], ["for thousand women", "2013", "45 to 49 years", "0.1", "0.4", "0.3", "0.3", "0.4", "0.7", "0.3", "0.4", "0.6", "0.6", "0.7", "0.7", "1.1", "0.6"], ["for thousand women", "2014", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["for thousand women", "2014", "15 to 19 years", "15.8", "9.4", "13.9", "17", "7.5", "7.7", "23.9", "28.2", "13.6", "6.7", "14.2", "35.7", "102.3", "10.2"], ["for thousand women", "2014", "20 to 24 years", "51.6", "51.5", "51.8", "62.2", "41.2", "32.8", "69.5", "75.6", "52.9", "30", "43.2", "73.9", "170.7", "41"], ["for thousand women", "2014", "25 to 29 years", "90.7", "112.3", "84.4", "106.7", "106.1", "81.5", "111.2", "122.6", "102.8", "74.2", "87.8", "103.6", "153.4", "92.1"], ["for thousand women", "2014", "30 to 34 years", "91.2", "105.7", "97", "95.7", "108.9", "108.4", "111.7", "115.1", "111.6", "100.7", "97.2", "107.1", "102.7", "107.6"], ["for thousand women", "2014", "35 to 39 years", "35.9", "42.2", "44.1", "33.3", "51.9", "57.5", "52.1", "50.9", "57.6", "57.6", "54.4", "45", "52.2", "54.6"], ["for thousand women", "2014", "40 to 44 years", "4.9", "6.4", "7.2", "4.8", "10.7", "11.7", "9.4", "9", "11.5", "12", "11.9", "10.1", "10.9", "10.9"], ["for thousand women", "2014", "45 to 49 years", "0.3", "0.8", "0.2", "0.1", "0.5", "0.7", "0.5", "0.3", "0.6", "0.7", "1.5", "0.7", "1", "0.6"], ["for thousand women", "2015", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["for thousand women", "2015", "15 to 19 years", "11.3", "10.5", "12.6", "15.3", "7.2", "7", "21.7", "24.9", "12.9", "6.1", "9.5", "31.1", "110.3", "9.4"], ["for thousand women", "2015", "20 to 24 years", "51.9", "48.8", "47.4", "60.3", "39.6", "30.9", "65.1", "72.7", "52.6", "28.7", "52.2", "69.8", "161.8", "39.3"], ["for thousand women", "2015", "25 to 29 years", "89.4", "104.6", "82.2", "101.2", "103.1", "78.7", "108", "117.2", "102.6", "71.8", "116.1", "98.8", "147.7", "89.5"], ["for thousand women", "2015", "30 to 34 years", "91.3", "96.7", "93.8", "91.3", "108.3", "107.4", "116.5", "114.4", "113.6", "101.2", "95.5", "116", "91.1", "107.4"], ["for thousand women", "2015", "35 to 39 years", "37.3", "41.7", "41.8", "34.5", "52.5", "59.5", "54.1", "50.2", "57.1", "57.9", "48.1", "54.1", "41.8", "55.6"], ["for thousand women", "2015", "40 to 44 years", "4.7", "6.7", "7.3", "4.3", "11.4", "12.1", "10", "10.2", "12", "12.7", "11.4", "14.2", "9.3", "11.5"], ["for thousand women", "2015", "45 to 49 years", "0.2", "0.2", "0.3", "0.1", "0.6", "0.8", "0.4", "0.4", "0.9", "0.8", "0.8", "1.4", "0", "0.7"], ["for thousand women", "2016", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""], ["for thousand women", "2016", "15 to 19 years", "12.5", "8.4", "11.3", "12.3", "6.7", "6.1", "20.5", "23.7", "11.2", "5.2", "6.8", "25", "110.2", "8.4"], ["for thousand women", "2016", "20 to 24 years", "48.3", "44.7", "48.2", "59.2", "38.7", "29.7", "63.4", "71.2", "48.5", "26.7", "51.7", "66.8", "162.7", "37.6"], ["for thousand women", "2016", "25 to 29 years", "87.8", "93.4", "81.1", "104.1", "100.9", "76.5", "105.5", "120", "98.1", "72.7", "75", "90.4", "151.9", "87.6"], ["for thousand women", "2016", "30 to 34 years", "91.8", "120.3", "94.6", "92.7", "107.9", "108.5", "114.4", "111.9", "111.3", "103.1", "105.1", "101.8", "99.6", "107.6"], ["for thousand women", "2016", "35 to 39 years", "37.8", "38.2", "42.3", "35.3", "52", "59", "55.2", "51.7", "58.8", "60.4", "73.3", "64.8", "47", "56"], ["for thousand women", "2016", "40 to 44 years", "5.9", "7.5", "7.1", "5.1", "11.3", "12", "10.7", "9.1", "12.5", "12.7", "9.9", "11.1", "21", "11.5"], ["for thousand women", "2016", "45 to 49 years", "0.2", "1", "0.2", "0.2", "0.6", "0.8", "0.5", "0.6", "0.7", "0.7", "1.4", "0.7", "1", "0.7"]]}, "native_split": "test", "question": "women of which age period had higher fertility rate in ontario in 2015,30 to 34 or 25 to 29?", "source_prompt": "{table}\n{question}", "answer_dp": "30 to 34 years", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_501_2750_4", "source_table_id": "501"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_341_totto38862-3_0_1", "source": "hitab", "table_key": "2430c57f93e212434cceff20c7a8936149fb1643", "grid": {"TD": 2, "LD": 2, "nrows": 4, "ncols": 8, "section_rows": [], "cells": [["", "", "heat", "heat", "semifinal", "semifinal", "final", "final"], ["", "", "result", "rank", "result", "rank", "result", "rank"], ["rose lokonyen", "800 m", "2:16.64", "7", "did not advance", "did not advance", "did not advance", "did not advance"], ["anjelina lohalith", "1500 m", "4:47.38", "14", "did not advance", "did not advance", "did not advance", "did not advance"]]}, "native_split": "train", "question": "what was the result that anjelina lohalith got in the 1500m in the heat?", "source_prompt": "{table}\n{question}", "answer_dp": "4:47.38", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_341_totto38862-3_0_1", "source_table_id": "341_totto38862-3"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1077_6172_1", "source": "hitab", "table_key": "efa0d2d380628024925298bb908bab4a54217db0", "grid": {"TD": 2, "LD": 1, "nrows": 12, "ncols": 6, "section_rows": [], "cells": [["", "2008", "2008", "2014", "2014", "variation of number"], ["", "number", "share", "number", "share", ""], ["between 1 and 4", "662589", "64.4", "707075", "64.9", "6.7"], ["between 5 and 9", "183979", "17.9", "187773", "17.2", "2.1"], ["between 10 and 19", "97574", "9.5", "102493", "9.4", "5"], ["between 20 and 49", "56411", "5.5", "61520", "5.6", "9.1"], ["between 50 and 99", "16991", "1.7", "18596", "1.7", "9.4"], ["between 100 and 249", "7566", "0.7", "8219", "0.8", "8.6"], ["between 250 and 499", "1770", "0.2", "1893", "0.2", "6.9"], ["between 500 and 999", "737", "0.1", "805", "0.1", "9.2"], ["1,000 and above", "780", "0.1", "762", "0.1", "-2.3"], ["total", "1028397", "100", "1089136", "100", "5.9"]]}, "native_split": "test", "question": "which enterprise size class has the highest relative increase in enterprise number?", "source_prompt": "{table}\n{question}", "answer_dp": "between 50 and 99", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1077_6172_1", "source_table_id": "1077"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_482_totto54940-1_0_1", "source": "hitab", "table_key": "33c63f2a1c878992c2c2554d46914fb56e52b63e", "grid": {"TD": 2, "LD": 2, "nrows": 14, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "national cup", "national cup", "league cup", "league cup", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["melbourne heart", "2011-12", "a-league", "25", "1", "0", "0", "0", "0", "0", "0", "25", "1"], ["newcastle united", "2012-13", "premier league", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["newcastle united", "2013-14", "premier league", "0", "0", "0", "0", "1", "0", "0", "0", "1", "0"], ["newcastle united", "2014-15", "premier league", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["newcastle united", "2015-16", "premier league", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["newcastle united", "2016-17", "championship", "0", "0", "1", "0", "0", "0", "0", "0", "1", "0"], ["newcastle united", "2017-18", "premier league", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["newcastle united", "total", "", "0", "0", "1", "0", "1", "0", "0", "0", "2", "0"], ["bradford city", "2012-13", "league two", "3", "0", "1", "0", "3", "0", "2", "0", "9", "0"], ["dundee united", "2013-14", "scottish premiership", "4", "1", "1", "0", "0", "0", "0", "0", "5", "1"], ["melbourne city", "2018-19", "a-league", "0", "0", "0", "0", "-", "-", "0", "0", "0", "0"], ["career total", "", "", "32", "2", "3", "0", "4", "0", "2", "0", "41", "2"]]}, "native_split": "test", "question": "which club did curtis good play for in 2011-12?", "source_prompt": "{table}\n{question}", "answer_dp": "melbourne heart", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_482_totto54940-1_0_1", "source_table_id": "482_totto54940-1"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_556_totto63673-2_0_1", "source": "hitab", "table_key": "ea283a7623b3faede70419a103a156447ca48042", "grid": {"TD": 2, "LD": 2, "nrows": 21, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "continental", "continental", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["dinamo tirana", "2006-07", "albanian superliga", "1", "0", "0", "0", "-", "-", "1", "0", "none", "none"], ["dinamo tirana", "2007-08", "albanian superliga", "0", "0", "0", "0", "-", "-", "1", "0", "none", "none"], ["dinamo tirana", "total", "", "1", "0", "0", "0", "-", "-", "1", "0", "none", "none"], ["belasitsa petrich", "2008-09", "a group", "20", "1", "0", "0", "-", "-", "20", "1", "none", "none"], ["tirana", "2009-10", "albanian superliga", "14", "0", "3", "0", "0", "0", "0", "0", "17", "0"], ["tirana", "2007-08", "albanian superliga", "-", "-", "1", "0", "-", "-", "1", "0", "none", "none"], ["tirana", "total", "", "14", "0", "3", "0", "1", "0", "0", "0", "18", "0"], ["skenderbeu korce", "2010-11", "albanian superliga", "23", "0", "3", "0", "-", "-", "26", "0", "none", "none"], ["skenderbeu korce", "2011-12", "albanian superliga", "19", "0", "8", "0", "2", "0", "1", "0", "30", "0"], ["skenderbeu korce", "2012-13", "albanian superliga", "24", "5", "7", "1", "2", "0", "1", "0", "34", "6"], ["skenderbeu korce", "2013-14", "albanian superliga", "27", "1", "7", "0", "2", "0", "1", "0", "37", "1"], ["skenderbeu korce", "total", "", "93", "6", "25", "1", "6", "0", "3", "0", "127", "7"], ["kukesi", "2014-15", "albanian superliga", "-", "-", "2", "0", "-", "-", "2", "0", "none", "none"], ["tractor sazi", "2014-15", "persian gulf pro league", "10", "1", "0", "0", "0", "0", "-", "-", "10", "1"], ["partizani tirana", "2015-16", "albanian superliga", "23", "1", "1", "0", "2", "0", "-", "-", "26", "1"], ["vllaznia shkoder", "2016-17", "albanian superliga", "29", "0", "4", "0", "-", "-", "33", "0", "none", "none"], ["flamurtari vlore", "2017-18", "albanian superliga", "25", "3", "5", "0", "-", "-", "30", "3", "none", "none"], ["teuta durres", "2018-19", "albanian superliga", "0", "0", "0", "0", "-", "-", "0", "0", "none", "none"], ["career total", "", "", "215", "12", "38", "1", "11", "0", "3", "0", "277", "13"]]}, "native_split": "test", "question": "in 2017-18 season, which league did bicaj play in side flamurtari vlore?", "source_prompt": "{table}\n{question}", "answer_dp": "albanian superliga", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_556_totto63673-2_0_1", "source_table_id": "556_totto63673-2"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_2627_14233_1", "source": "hitab", "table_key": "c2ecccb7a0633ca6c89a7251c69ade51c6bbfa4e", "grid": {"TD": 1, "LD": 3, "nrows": 50, "ncols": 10, "section_rows": [1, 4, 12, 15, 18, 21, 24, 27, 30, 35, 38], "cells": [["", "", "", "police", "school system", "banks", "justice system and courts", "media", "federal parliament", "major corporations"], ["predicted probabilities", "sex", "", "", "", "", "", "", "", ""], ["predicted probabilities", "sex", "male", "0.74", "0.6", "0.56", "0.56", "0.4", "0.35", "0.28"], ["predicted probabilities", "sex", "female", "0.79", "0.62", "0.62", "0.58", "0.4", "0.38", "0.31"], ["predicted probabilities", "age group", "", "", "", "", "", "", "", ""], ["predicted probabilities", "age group", "15 to 24 years", "0.76", "0.63", "0.67", "0.63", "0.32", "0.49", "0.31"], ["predicted probabilities", "age group", "25 to 34 years", "0.72", "0.6", "0.5", "0.52", "0.36", "0.34", "0.24"], ["predicted probabilities", "age group", "35 to 44 years", 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"0.63", "0.6", "0.44", "0.31", "0.29"], ["predicted probabilities", "province of residence", "new brunswick", "0.8", "0.69", "0.63", "0.65", "0.49", "0.35", "0.35"], ["predicted probabilities", "province of residence", "quebec", "0.74", "0.61", "0.54", "0.54", "0.43", "0.39", "0.27"], ["predicted probabilities", "province of residence", "ontario", "0.77", "0.61", "0.6", "0.62", "0.39", "0.37", "0.29"], ["predicted probabilities", "province of residence", "manitoba", "0.78", "0.65", "0.63", "0.51", "0.4", "0.37", "0.36"], ["predicted probabilities", "province of residence", "saskatchewan", "0.79", "0.66", "0.64", "0.61", "0.43", "0.38", "0.33"], ["predicted probabilities", "province of residence", "alberta", "0.78", "0.63", "0.61", "0.57", "0.36", "0.38", "0.3"], ["predicted probabilities", "province of residence", "british columbia", "0.73", "0.57", "0.57", "0.49", "0.38", "0.32", "0.28"], ["predicted probabilities", "total", "", "0.76", "0.61", "0.59", "0.57", "0.4", "0.37", "0.29"]]}, "native_split": "test", "question": "which group of people had a lower probability of confidence in police? visible minorities or non-visible minorities?", "source_prompt": "{table}\n{question}", "answer_dp": "visible minority", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_2627_14233_1", "source_table_id": "2627"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_165_totto19654-4_0_1", "source": "hitab", "table_key": "eab2bcfdba2c2e17e27111a29cdc77e44250661c", "grid": {"TD": 2, "LD": 2, "nrows": 11, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "fa cup", "fa cup", "league cup", "league cup", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["colchester united", "2014-15", "league one", "0", "0", "0", "0", "0", "0", "0", "0", "0", "0"], ["welling united", "2015-16", "national league", "38", "1", "3", "0", "-", "-", "1", "0", "42", "1"], ["maidstone united", "2016-17", "national league", "41", "4", "3", "0", "-", "-", "2", "0", "46", "4"], ["stevenage", "2017-18", "league two", "2", "0", "-", "-", "0", "0", "0", "0", "2", "0"], ["dagenham & redbridge", "2017-18", "national league", "25", "2", "2", "0", "-", "-", "1", "0", "28", "2"], ["dover athletic", "2017-18", "national league", "12", "1", "-", "-", "12", "1", "none", "none", "none", "none"], ["dover athletic", "2018-19", "national league", "39", "6", "2", "0", "-", "-", "1", "0", "42", "6"], ["dover athletic", "total", "", "51", "7", "2", "0", "-", "-", "1", "0", "54", "7"], ["career total", "", "", "157", "14", "10", "0", "0", "0", "5", "0", "172", "14"]]}, "native_split": "test", "question": "which club did lokko play for in 2015-16?", "source_prompt": "{table}\n{question}", "answer_dp": "welling united", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_165_totto19654-4_0_1", "source_table_id": "165_totto19654-4"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_240_totto27228-1_0_1", "source": "hitab", "table_key": "65a1e3ae82a4e19f3155f52817e359ea95750a46", "grid": {"TD": 2, "LD": 2, "nrows": 13, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "continental", "continental", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["independiente", "1997-98", "argentine primera division", "5", "0", "0", "0", "-", "-", "0", "0", "5", "0"], ["toros neza", "2000-01", "primera division a", "15", "1", "0", "0", "-", "-", "0", "0", "15", "1"], ["america de cali", "2001", "categoria primera a", "3", "0", "0", "0", "0", "0", "0", "0", "3", "0"], ["argentinos juniors", "2001-02", "argentine primera division", "33", "1", "0", "0", "-", "-", "0", "0", "33", "1"], ["nueva chicago", "2002-03", "argentine primera division", "34", "1", "0", "0", "-", "-", "0", "0", "34", "1"], ["nueva chicago", "2005-06", "primera b nacional", "9", "0", "0", "0", "-", "-", "0", "0", "9", "0"], ["nueva chicago", "total", "", "43", "1", "0", "0", "-", "-", "0", "0", "43", "1"], ["jorge wilstermann", "2006", "bolivian primera division", "8", "0", "0", "0", "-", "-", "0", "0", "8", "0"], ["alumni", "2007-08", "torneo argentino a", "22", "1", "0", "0", "-", "-", "0", "0", "22", "1"], ["tiro federal", "2008-09", "torneo argentino b", "15", "1", "0", "0", "-", "-", "0", "0", "15", "1"], ["career total", "", "", "144", "5", "0", "0", "0", "0", "0", "0", "144", "5"]]}, "native_split": "test", "question": "in the 2001 season, which league did garcia join in categoria for america de cali?", "source_prompt": "{table}\n{question}", "answer_dp": "categoria primera a", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_240_totto27228-1_0_1", "source_table_id": "240_totto27228-1"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_340_totto38744-4_0_1", "source": "hitab", "table_key": "5ffe5d47ee74dd58005d2274c4ab5465918fd81e", "grid": {"TD": 2, "LD": 3, "nrows": 27, "ncols": 11, "section_rows": [], "cells": [["", "", "", "league", "league", "fa cup", "fa cup", "other", "other", "total", "total"], ["", "", "", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["kalmar", "1985", "allsvenskan", "1", "0", "none", "none", "none", "none", "none", "none"], ["kalmar", "1986", "allsvenskan", "17", "0", "none", "none", "none", "none", "none", "none"], ["kalmar", "1987", "swedish football division 1", "16", "3", "none", "none", "none", "none", "none", "none"], ["kalmar", "total", "", "34", "3", "none", "none", "none", "none", "none", "none"], ["osters", "1988", "allsvenskan", "20", "2", "none", "none", "none", "none", "none", "none"], ["osters", "1989", "swedish football division 1", "26", "14", "none", "none", "none", "none", "none", "none"], ["osters", "1990", "allsvenskan", "22", "5", "none", "none", "none", "none", "none", "none"], ["osters", "1991", "allsvenskan", "31", "13", "none", "none", "none", "none", "none", "none"], ["osters", "1992", "allsvenskan", "28", "8", "none", "none", "none", "none", "none", "none"], ["osters", "total", "", "127", "42", "none", "none", "none", "none", "none", "none"], ["norrkoping", "1993", "allsvenskan", "23", "8", "none", "none", "none", "none", "none", "none"], ["norrkoping", "1994", "allsvenskan", "19", "0", "none", "none", "none", "none", "none", "none"], ["norrkoping", "1995", "allsvenskan", "25", "0", "none", "none", "none", "none", "none", "none"], ["norrkoping", "1996", "allsvenskan", "6", "1", "none", "none", "none", "none", "none", "none"], ["norrkoping", "1997", "allsvenskan", "9", "2", "none", "none", "none", "none", "none", "none"], ["norrkoping", "total", "", "82", "11", "none", "none", "none", "none", "none", "none"], ["port vale", "1996-97", "first division", "11", "1", "1", "0", "0", "0", "12", "1"], ["port vale", "1997-98", "first division", "33", "5", "0", "0", "2", "0", "35", "5"], ["port vale", "1998-99", "first division", "7", "0", "0", "0", "1", "0", "8", "0"], ["port vale", "total", "", "51", "6", "1", "0", "3", "0", "55", "6"], ["norrkoping", "1999", "allsvenskan", "4", "0", "none", "none", "none", "none", "none", "none"], ["norrkoping", "2000", "allsvenskan", "5", "0", "none", "none", "none", "none", "none", "none"], ["norrkoping", "2001", "allsvenskan", "6", "0", "none", "none", "none", "none", "none", "none"], ["norrkoping", "total", "", "15", "0", "none", "none", "none", "none", "none", "none"], ["total", "", "", "309", "62", "1", "0", "3", "0", "313", "62"]]}, "native_split": "test", "question": "which club did jansson play for in 1985?", "source_prompt": "{table}\n{question}", "answer_dp": "kalmar", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_340_totto38744-4_0_1", "source_table_id": "340_totto38744-4"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_60_218_2", "source": "hitab", "table_key": "0167d318fdc39314616f09d43ff15dac671aae13", "grid": {"TD": 2, "LD": 2, "nrows": 44, "ncols": 10, "section_rows": [2, 6, 13, 16, 22, 29, 33, 38], "cells": [["", "", "agricultural region 1", "agricultural region 1", "agricultural region 3", "agricultural region 3", "agricultural region 4", "agricultural region 4", "total new brunswick", "total new brunswick"], ["", "", "number", "percent", "number", "percent", "number", "percent", "number", "percent"], ["cattle ranching and farming", "", "186", "20.9", "160", "26.8", "38", "9.6", "586", "22.4"], ["cattle ranching and farming", "beef cattle ranching and farming, including feedlots", "129", "14.5", "108", "18.1", "11", "2.8", "358", "13.7"], ["cattle ranching and farming", "dairy cattle and milk production", "57", "6.4", "52", "8.7", "27", "6.9", "228", "8.7"], ["hog and pig farming", "", "4", "0.4", "3", "0.5", "3", "0.8", "16", "0.6"], ["poultry and egg production", "", "22", "2.5", "12", "2", "3", "0.8", "50", "1.9"], ["poultry and egg production", "chicken egg production", "15", "1.7", "6", "1", "3", "0.8", "33", "1.3"], ["poultry and egg production", "broiler and other meat-type chicken production", "6", "0.7", "4", "0.7", "0", "0", "11", "0.4"], ["poultry and egg production", "turkey production", "0", "0", "0", "0", "0", "0", "1", "0"], ["poultry and egg production", "poultry hatcheries", "1", "0.1", "0", "0", "0", "0", "1", "0"], ["poultry and egg production", "combination poultry and egg production", "0", "0", "1", "0.2", "0", "0", "3", "0.1"], ["poultry and egg production", "all other poultry production", "0", "0", "1", "0.2", "0", "0", "1", "0"], ["sheep and goat farming", "", "14", "1.6", "21", "3.5", "4", "1", "57", "2.2"], ["sheep and goat farming", "sheep farming", "9", "1", "15", "2.5", "1", "0.3", "38", "1.5"], ["sheep and goat farming", "goat farming", "5", "0.6", "6", "1", "3", "0.8", "19", "0.7"], ["other animal production", "", "101", "11.4", "82", "13.7", "27", "6.9", "317", "12.1"], ["other animal production", "apiculture", "10", "1.1", "15", "2.5", "6", "1.5", "44", "1.7"], ["other animal production", "horse and other equine production", "40", "4.5", "31", "5.2", "12", "3", "128", "4.9"], ["other animal production", "fur-bearing animal and rabbit production", "9", "1", "7", "1.2", "2", "0.5", "20", "0.8"], ["other animal production", "animal combination farming", "39", "4.4", "25", "4.2", "7", "1.8", "113", "4.3"], ["other animal production", "all other miscellaneous animal production", "3", "0.3", "4", "0.7", "0", "0", "12", "0.5"], ["oilseed and grain farming", "", "40", "4.5", "6", "1", "6", "1.5", "59", "2.3"], ["oilseed and grain farming", "soybean farming", "10", "1.1", "2", "0.3", "1", "0.3", "13", "0.5"], ["oilseed and grain farming", "oilseed (except soya) farming", "2", "0.2", "0", "0", "0", "0", "2", "0.1"], ["oilseed and grain farming", "dry pea and bean farming", "1", "0.1", "0", "0", "0", "0", "1", 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"other food crops grown under cover", "6", "0.7", "3", "0.5", "0", "0", "17", "0.7"], ["greenhouse, nursery and floriculture production", "nursery and tree production", "78", "8.8", "18", "3", "18", "4.6", "162", "6.2"], ["greenhouse, nursery and floriculture production", "floriculture production", "11", "1.2", "14", "2.3", "16", "4.1", "60", "2.3"], ["other crop farming", "", "206", "23.2", "141", "23.6", "106", "26.9", "627", "24"], ["other crop farming", "tobacco farming", "0", "0", "0", "0", "0", "0", "0", "0"], ["other crop farming", "hay farming", "135", "15.2", "96", "16.1", "45", "11.4", "406", "15.5"], ["other crop farming", "fruit and vegetable combination farming", "10", "1.1", "5", "0.8", "2", "0.5", "27", "1"], ["other crop farming", "maple syrup and products production", "36", "4", "17", "2.8", "45", "11.4", "108", "4.1"], ["other crop farming", "all other miscellaneous crop farming", "25", "2.8", "23", "3.9", "14", "3.6", "86", "3.3"]]}, "native_split": "test", "question": "which region had fewer farms producing fruit and tree-nuts compared with the province as a whole.", "source_prompt": "{table}\n{question}", "answer_dp": "agricultural region 1", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_60_218_2", "source_table_id": "60"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_1510_8388_1(1)", "source": "hitab", "table_key": "95528a53b8aa2decfbee9352d1e21edb623b0ef3", "grid": {"TD": 1, "LD": 2, "nrows": 18, "ncols": 5, "section_rows": [], "cells": [["", "", "women", "men", "total"], ["percent", "crime was minor and not worth taking the time to report", "71", "75", "71"], ["percent", "incident was a private or personal matter and it was handled informally", "68", "62", "67"], ["percent", "no one was harmed", "66", "42", "63"], ["percent", "did not want the hassle of dealing with the police", "47", "33", "45"], ["percent", "police would not have considered incident important enough", "43", "43", "43"], ["percent", "lack of evidence", "46", "18", "43"], ["percent", "offender would not be convicted or adequately punished", "41", "27", "40"], ["percent", "no harm was intended", "38", "49", "39"], ["percent", "feared or did not want the hassle of dealing with the court process", "34", "37", "34"], ["percent", "did not want the offender in trouble with the law", "29", "36", "30"], ["percent", "did not want others to find out about the victimization", "32", "", "30"], ["percent", "police would not have been efficient or effective", "27", "", "26"], ["percent", "fear of revenge by the offender or others", "24", "", "22"], ["percent", "police would not have been able to find or identify the offender", "22", "", "21"], ["percent", "police would be biased", "13", "", "13"], ["percent", "received unsatisfactory service from the police in the past", "13", "", "13"], ["percent", "afraid reporting would bring shame and dishonour to the family", "13", "", "12"]]}, "native_split": "test", "question": "which sex of people were more likely to state that they did not report to the police because there was a lack of evidence?", "source_prompt": "{table}\n{question}", "answer_dp": "women", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_1510_8388_1(1)", "source_table_id": "1510"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_895_5139_2", "source": "hitab", "table_key": "c519b9f5f423ad5a622b2352bbe5bbc24f70bde7", "grid": {"TD": 3, "LD": 1, "nrows": 13, "ncols": 10, "section_rows": [], "cells": [["", "all", "all", "all", "men", "men", "men", "women", "women", "women"], ["", "2011", "2015", "change", "2011", "2015", "change", "2011", "2015", "change"], ["", "percent", "percent", "percentage point", "percent", "percent", "percentage point", "percent", "percent", "percentage point"], ["united states, united kingdom, ireland, australia, new zealand", "78.6", "84.3", "5.7", "77.4", "85.1", "7.7", "79.6", "83.6", "4"], ["caribbean, central/south america", "65.1", "72.5", "7.4", "65.4", "73", "7.6", "64.7", "72.2", "7.5"], ["western/northern europe", "79.3", "82.6", "3.3", "81.5", "84", "2.5", "77.1", "81.3", "4.2"], ["southern europe", "73.1", "72.5", "-0.6", "74.2", "73.6", "-0.6", "72.1", "71.4", "-0.7"], ["eastern europe", "66.7", "69.7", "3", "66.7", "70.6", "3.9", "66.7", "68.9", "2.2"], ["south asia", "72.4", "80.5", "8.1", "73.7", "79.5", "5.8", "71.1", "81.5", "10.4"], ["southeast asia", "64.5", "74.6", "10.1", "66.1", "74.1", "8", "63.3", "75", "11.7"], ["east asia", "59.8", "64.1", "4.3", "60.3", "65.4", "5.1", "59.3", "62.9", "3.6"], ["west central asia and middle east", "56.7", "73.2", "16.5", "56", "69.9", "13.9", "57.6", "76.5", "18.9"], ["africa", "67.1", "78.4", "11.3", "72.7", "79.4", "6.7", "60.4", "77.3", "16.9"]]}, "native_split": "test", "question": "in 2015, where were immigrants from had the second-highest turnout rate?", "source_prompt": "{table}\n{question}", "answer_dp": "western/northern europe", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_895_5139_2", "source_table_id": "895"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_524_2936_2", "source": "hitab", "table_key": "57a7df935ef13a33c8c929d5249b25e3e8379473", "grid": {"TD": 3, "LD": 3, "nrows": 48, "ncols": 7, "section_rows": [4, 11, 14, 18, 20, 23, 37, 42], "cells": [["", "", "", "first tuberculosis-related hospitalization after landing", "first tuberculosis-related hospitalization after landing", "first tuberculosis-related hospitalization after landing", "first tuberculosis-related hospitalization after landing"], ["", "", "", "number", "incidence adjusted for person-years", "95% confidence interval", "95% confidence interval"], ["", "", "", "", "", "from", "to"], ["all", "", "", "1015", "5.2", "4.8", "5.5"], ["age at hospitalization", "", "", "", "", "", ""], ["age at hospitalization", "0 to 17 years", "", "65", "1.5", "0.8", "2.1"], ["age at hospitalization", "18 to 24 years", "", "175", "3.4", "2.5", "4.2"], ["age at hospitalization", "25 to 34 years", "", "270", "7.1", "6.1", "8"], ["age at hospitalization", "35 to 44 years", "", "165", "5.8", "5", "6.5"], ["age at hospitalization", "45 to 64 years", "", "190", "3.4", "3.2", "3.9"], ["age at hospitalization", "65 years and older", "", "145", "11.3", "9.8", "12.8"], ["sex", "", "", "", "", "", ""], ["sex", "male", "", "535", "5.6", "5.1", "6.1"], ["sex", "female", "", "480", "4.7", "4.3", "5.2"], ["landing year", "", "", "", "", "", ""], ["landing year", "2000 to 2003", "", "415", "4.5", "4.1", "5"], ["landing year", "2004 to 2008", "", "410", "5.3", "4.8", "5.8"], ["landing year", "2009 to 2013", "", "195", "6.8", "5.9", "7.8"], ["country risk level", "", "", "", "", "", ""], ["country risk level", "low", "", "30", "0.6", "0.3", "0.8"], ["country risk level", "high", "", "985", "6.8", "6.3", "7.2"], ["country risk level", "high", "who", "785", "6.9", "6.4", "7.4"], ["country risk level", "high", "ircc-specific", "200", "6.2", "5.4", "7.2"], ["top 12 source countries for tuberculosis hospitalizations in canada", "", "", "", "", "", ""], ["top 12 source countries for tuberculosis hospitalizations in canada", "india", "", "245", "8.5", "7.4", "9.6"], ["top 12 source countries for tuberculosis hospitalizations in canada", "philippines", "", "175", "10.6", "9.1", "12.4"], ["top 12 source countries for tuberculosis hospitalizations in canada", "china", "", "70", "2.1", "1.6", "2.7"], ["top 12 source countries for tuberculosis hospitalizations in canada", "ethiopia", "", "65", "51.1", "39.7", "65.7"], ["top 12 source countries for tuberculosis hospitalizations in canada", "pakistan", "", "45", "3.8", "2.8", "5.2"], ["top 12 source countries for tuberculosis hospitalizations in canada", "somalia", "", "45", "42.8", "31.3", "58.5"], ["top 12 source countries for tuberculosis hospitalizations in canada", "sri lanka", "", "30", "7.4", "5.1", "10.6"], ["top 12 source countries for tuberculosis hospitalizations in canada", "afghanistan", "", "35", "14.8", "10.4", "21"], ["top 12 source countries for tuberculosis hospitalizations in canada", "viet nam", "", "30", "17.5", "12.1", "25.1"], ["top 12 source countries for tuberculosis hospitalizations in canada", "nepal", "", "25", "42.4", "27.8", "64.1"], ["top 12 source countries for tuberculosis hospitalizations in canada", "sudan", "", "30", "24.3", "16.5", "35.7"], ["top 12 source countries for tuberculosis hospitalizations in canada", "bangladesh", "", "25", "8.2", "5.2", "12.6"], ["top 12 source countries for tuberculosis hospitalizations in canada", "all others", "", "205", "2.2", "1.9", "2.5"], ["intended region of residence", "", "", "", "", "", ""], ["intended region of residence", "atlantic", "", "20", "4.7", "2.9", "7.5"], ["intended region of residence", "ontario", "", "635", "5", "4.6", "5.4"], ["intended region of residence", "prairies", "", "220", "8", "7", "9.2"], ["intended region of residence", "british columbia", "", "140", "3.7", "3.1", "4.3"], ["immigrant category", "", "", "", "", "", ""], ["immigrant category", "economic, principal applicants", "", "140", "3.1", "2.6", "3.6"], ["immigrant category", "economic, spouse/children", "", "175", "2.5", "2.1", "2.9"], ["immigrant category", "family", "", "390", "7.1", "6.4", "7.8"], ["immigrant category", "refugee", "", "290", "13.5", "12", "15.2"], ["immigrant category", "other/unknown/missing", "", "20", "5.6", "3.5", "8.9"]]}, "native_split": "test", "question": "which intended region of residence had the highest incidence?", "source_prompt": "{table}\n{question}", "answer_dp": "prairies", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_524_2936_2", "source_table_id": "524"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_313_totto35910-0_0_1", "source": "hitab", "table_key": "1d3711ed7f3dc4634e31233a85bcac76eb9b4842", "grid": {"TD": 2, "LD": 2, "nrows": 13, "ncols": 11, "section_rows": [], "cells": [["", "", "league", "league", "league", "national cup", "national cup", "continental", "continental", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["austria wien", "2011-12", "austrian bundesliga", "13", "2", "1", "0", "none", "none", "14", "2"], ["austria wien", "2012-13", "austrian bundesliga", "28", "3", "4", "0", "none", "none", "32", "3"], ["austria wien", "2013-14", "austrian bundesliga", "10", "1", "1", "0", "4", "0", "15", "1"], ["austria wien", "total", "", "51", "6", "6", "0", "4", "0", "61", "6"], ["tobol", "2014", "kazakhstan premier league", "26", "5", "0", "0", "none", "none", "26", "5"], ["tobol", "2015", "kazakhstan premier league", "27", "5", "4", "1", "none", "none", "30", "6"], ["tobol", "2016", "kazakhstan premier league", "28", "8", "0", "0", "none", "none", "28", "8"], ["tobol", "2017", "kazakhstan premier league", "6", "0", "1", "0", "none", "none", "7", "0"], ["tobol", "total", "", "87", "18", "5", "1", "none", "none", "92", "19"], ["aktobe", "2017", "kazakhstan premier league", "14", "5", "0", "0", "none", "none", "14", "5"], ["career total", "", "", "152", "29", "11", "1", "4", "0", "167", "30"]]}, "native_split": "test", "question": "which club did simkovic play for in 2014?", "source_prompt": "{table}\n{question}", "answer_dp": "tobol", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_313_totto35910-0_0_1", "source_table_id": "313_totto35910-0"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_255_totto28917-2_0_1", "source": "hitab", "table_key": "12f0e62e2a5c8fc32b4e2962a01563c859c88c71", "grid": {"TD": 2, "LD": 2, "nrows": 17, "ncols": 13, "section_rows": [], "cells": [["", "", "league", "league", "league", "cup", "cup", "continental", "continental", "other", "other", "total", "total"], ["", "", "division", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals", "apps", "goals"], ["acassuso", "2011-12", "primera b metropolitana", "32", "4", "0", "0", "-", "-", "32", "4", "none", "none"], ["acassuso", "2012-13", "primera b metropolitana", "11", "1", "0", "0", "-", "-", "11", "1", "none", "none"], ["acassuso", "2013-14", "primera b metropolitana", "14", "0", "0", "0", "-", "-", "14", "0", "none", "none"], ["acassuso", "2014", "primera b metropolitana", "21", "4", "0", "0", "-", "-", "21", "4", "none", "none"], ["acassuso", "total", "", "78", "9", "0", "0", "-", "-", "78", "9", "none", "none"], ["colon", "2015", "primera division", "20", "5", "0", "0", "-", "-", "20", "5", "none", "none"], ["argentinos juniors", "2016", "primera division", "13", "1", "0", "0", "-", "-", "13", "1", "none", "none"], ["argentinos juniors", "2016-17", "primera b nacional", "42", "15", "2", "1", "-", "-", "44", "16", "none", "none"], ["argentinos juniors", "2017-18", "primera division", "11", "5", "0", "0", "-", "-", "11", "5", "none", "none"], ["argentinos juniors", "total", "", "66", "21", "2", "1", "-", "-", "68", "22", "none", "none"], ["independiente", "2017-18", "primera division", "7", "1", "0", "0", "2", "0", "-", "-", "9", "1"], ["independiente", "2018-19", "primera division", "12", "3", "2", "0", "2", "0", "-", "-", "16", "3"], ["independiente", "total", "", "19", "4", "2", "0", "4", "0", "-", "-", "25", "4"], ["athletico paranaense", "2019", "serie a", "0", "0", "0", "0", "3", "0", "-", "-", "3", "0"], ["career total", "", "", "183", "39", "4", "1", "7", "0", "0", "0", "194", "40"]]}, "native_split": "test", "question": "in 2016, which club did romero play in primera division?", "source_prompt": "{table}\n{question}", "answer_dp": "argentinos juniors", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_255_totto28917-2_0_1", "source_table_id": "255_totto28917-2"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_316_1645_2", "source": "hitab", "table_key": "cf853c29f8a32f3bdc3e8d1236eb5bf69515fc40", "grid": {"TD": 3, "LD": 2, "nrows": 29, "ncols": 8, "section_rows": [3, 8, 14, 19, 24], "cells": [["", "", "men", "men", "men", "women", "women", "women"], ["", "", "hazard ratio", "95% confidence interval", "95% confidence interval", "hazard ratio", "95% confidence interval", "95% confidence interval"], ["", "", "", "from", "to", "", "from", "to"], ["smoking", "", "", "", "", "", "", ""], ["smoking", "non-smoker", "1", "", "", "1", "", ""], ["smoking", "light smoker", "1.92", "1.51", "2.33", "1.81", "1.52", "2.11"], ["smoking", "heavy smoker", "2.36", "1.84", "2.89", "2.91", "1.92", "3.91"], ["smoking", "former smoker", "1.23", "1.05", "1.4", "1.31", "1.16", "1.46"], ["body mass index (bmi), with correction", "", "", "", "", "", "", ""], ["body mass index (bmi), with correction", "underweight", "1.77", "1.06", "2.47", "1.5", "1.16", "1.85"], ["body mass index (bmi), with correction", "normal weight", "1", "", "", "1", "", ""], ["body mass index (bmi), with correction", "overweight", "0.87", "0.79", "0.95", "0.86", "0.78", "0.94"], ["body mass index (bmi), with correction", "obese i", "0.96", "0.85", "1.07", "0.91", "0.8", "1.02"], ["body mass index (bmi), with correction", "obese ii", "1.51", "1.25", "1.76", "1.2", "1", "1.4"], ["alcohol consumption", "", "", "", "", "", "", ""], ["alcohol consumption", "light or non-drinker", "1.2", "1.09", "1.31", "1.15", "1.01", "1.29"], ["alcohol consumption", "moderate drinker", "1", "", "", "1", "", ""], ["alcohol consumption", "heavy drinker", "1.35", "1.06", "1.63", "2.29", "0.41", "4.16"], ["alcohol consumption", "former drinker", "1.65", "1.46", "1.83", "1.56", "1.36", "1.75"], ["minutes of physical activity per day", "", "", "", "", "", "", ""], ["minutes of physical activity per day", "none", "1.89", "1.61", "2.16", "2.04", "1.65", "2.43"], ["minutes of physical activity per day", "less than 30", "1.23", "1.08", "1.38", "1.22", "1.03", "1.41"], ["minutes of physical activity per day", "30 to 60", "0.99", "0.86", "1.12", "1.1", "0.92", "1.28"], ["minutes of physical activity per day", "more than 60", "1", "", "", "1", "", ""], ["fruit and vegetable servings per day", "", "", "", "", "", "", ""], ["fruit and vegetable servings per day", "none", "1.93", "0.49", "3.36", "1.71", "0.57", "2.85"], ["fruit and vegetable servings per day", "less than 2", "1.52", "1.3", "1.74", "1.82", "1.51", "2.13"], ["fruit and vegetable servings per day", "2 to 5", "1.18", "1.03", "1.32", "1.18", "1.05", "1.32"], ["fruit and vegetable servings per day", "more than 5", "1", "", "", "1", "", ""]]}, "native_split": "test", "question": "who had elevated hrs, light smokers or non-smokers?", "source_prompt": "{table}\n{question}", "answer_dp": "light smoker", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_316_1645_2", "source_table_id": "316"}, "dataset": "hitab", "subtype": "span"}
{"id": "hitab:hitab_494_2710_1", "source": "hitab", "table_key": "b75f09b1b939602cfe8faaf4c5d20d9629727493", "grid": {"TD": 1, "LD": 3, "nrows": 26, "ncols": 11, "section_rows": [1, 4, 6, 8, 13, 24], "cells": [["", "", "", "hge", "mge", "lge", "nf_nex", "of_next", "nf_ext", "of_ext", "full sample"], ["averages", "management practices and long-term strategies", "", "", "", "", "", "", "", "", ""], ["averages", "management practices and long-term strategies", "management practices index", "0.42", "0.41", "0.4", "0.42", "0.35", "0.43", "0.52", "0.4"], ["averages", "management practices and long-term strategies", "long-term strategic directions index", "0.42", "0.44", "0.43", "0.41", "0.4", "0.47", "0.51", "0.43"], ["averages", "size of enterprise", "", "", "", "", "", "", "", "", ""], ["averages", "size of enterprise", "number of employees", "58.7", "54", "41", "42.9", "41", "47.9", "66.3", "47.2"], ["averages", "employment growth", "", "", "", "", "", "", "", "", ""], ["averages", "employment growth", "annual growth based on 2009-2012 employment", "35.6", "11.2", "-1.9", "12.5", "2.4", "23.8", "8.7", "6.8"], ["averages", "economic performance", "", "", "", "", "", "", "", "", ""], ["averages", "economic performance", "sales per employee", "210231", "303046", "242791", "275331", "231402", "207793", "330219", "256959"], ["averages", "economic performance", "net profit per employee", "7758", "11288", "9177", "7351", "10886", "3352", "10035", "9637"], ["averages", "economic performance", "innovative enterprises, at least one type of innovation", "68.1", "78", "58.9", "66.2", "56.5", "79.5", "85.6", "65.8"], ["averages", "economic performance", "number of innovative products and processes", "1.7", "8.2", "1.2", "0.4", "1", "2.3", "12.3", "3.4"], ["averages", "other enterprise characteristics", "", "", "", "", "", "", "", "", ""], ["averages", "other enterprise characteristics", "performs r&d", "21", "39.2", "38.6", "24.6", "33.6", "23.5", "57.1", "36.6"], ["averages", "other enterprise characteristics", "physical and intangible assets per employee", "110512", "87650", "136575", "46134", "120094", "147442", "151897", "118478"], ["averages", "other enterprise characteristics", "share of profit centres", "15.5", "20.3", "21.4", "19.5", "18.1", "14", "29", "20.3"], ["averages", "other enterprise characteristics", "average age of enterprise", "11.2", "17.1", "18.8", "8.3", "20", "7.7", "19.4", "17.3"], ["averages", "other enterprise characteristics", "share of employees with a university degree", "13.8", "18", "17", "14", "16", "17.8", "20.7", "16.9"], ["averages", "other enterprise characteristics", "operations outside canada", "12.8", "26.4", "20.3", "0.1", "16.3", "23.7", "43.1", "21.2"], ["averages", "other enterprise characteristics", "share of competitors in the main market", "4.6", "4.9", "4.9", "5", "4.8", "5.1", "5", "4.9"], ["averages", "other enterprise characteristics", "share of enterprises in the manufacturing sector", "12", "19.8", "19", "7.8", "18.9", "11.4", "26.4", "18.4"], ["averages", "other enterprise characteristics", "share of enterprises in the service-producing sector", "51.4", "61.1", "61.9", "75.1", "57.1", "53.7", "61.5", "60.3"], ["averages", "other enterprise characteristics", "share of enterprises in the rest of the economy", "36.6", "19.1", "19.1", "17.1", "24", "34.9", "12.1", "21.3"], ["averages", "number of enterprises", "", "", "", "", "", "", "", "", ""], ["averages", "number of enterprises", "non-weighted total", "313", "919", "1719", "270", "1639", "164", "878", "2951"]]}, "native_split": "test", "question": "which have a higher percentage of innovative enterprises, moderate-growth enterprises or total avervage?", "source_prompt": "{table}\n{question}", "answer_dp": "mge", "answer_type": "span", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "hitab_494_2710_1", "source_table_id": "494"}, "dataset": "hitab", "subtype": "span"}
{"id": "tablebench:dp-test:d4a5c36f72e87f2eeac0751416cafcb4", "source": "tablebench", "table_key": "ad7afd3c4392b60ea6d39209e92b227732ba7081", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 5, "section_rows": [], "cells": [["sno", "power plant", "state", "total capacity (mw)", "completion schedule"], ["1", "kishenganga", "jammu & kashmir", "330", "2016"], ["2", "parbati - ii", "himachal pradesh", "800", "2013"], ["3", "subansiri (lower)", "assam", "2000", "2014"], ["4", "teesta low dam - iv", "west bengal", "160", "2011"], ["5", "parbati - iii", "himachal pradesh", "520", "2012"], ["6", "nimmo - bazgo", "jammu & kashmir", "45", "2011"], ["7", "chutak", "jammu & kashmir", "44", "2011"], ["8", "uri - ii", "jammu & kashmir", "240", "2011"]]}, "native_split": "test", "question": "Does a higher total capacity (mw) causally influence the completion schedule for the power plants listed in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: Yes, Higher interest positively influences deposit balances change (correlation coefficient of 0.89).\",\nFinal Answer: No, Analysis reveals a negligible inverse correlation (0.21), suggesting the gdp does not causally influence the population.\nFinal Answer: The water level of a river exhibits a stronger causal relationship with rainfall (0.82) compared to snowfall (0.32).\n\nEnsure the final answer format is the last output line and the answer should give the conclusion then provide a brief explanation of the causal analysis results as concise as possible.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Yes, a higher total capacity (MW) indicates a weak positive influences (correlation coefficient of 0.48) the completion schedule for the power plants.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d4a5c36f72e87f2eeac0751416cafcb4"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:3d4a4379fab0e72179a4a20199c27a18", "source": "tablebench", "table_key": "fc28f957771d04c71d204d874fc6af22c813e16b", "grid": {"TD": 1, "LD": 0, "nrows": 29, "ncols": 6, "section_rows": [], "cells": [["member state", "population in millions", "population % of eu", "area km 2", "area % of eu", "pop density people / km 2"], ["european union", "494.8", "100%", "4422773", "100%", "112.0"], ["austria", "8.3", "1.7%", "83858", "1.9%", "99.0"], ["belgium", "10.5", "2.1%", "30510", "0.7%", "344.0"], ["bulgaria", "7.7", "1.6%", "110912", "2.5%", "70.0"], ["croatia", "4.3", "0.9%", "56594", "1.3%", "75.8"], ["cyprus", "0.8", "0.2%", "9250", "0.2%", "84.0"], ["czech republic", "10.3", "2.1%", "78866", "1.8%", "131.0"], ["denmark", "5.4", "1.1%", "43094", "1.0%", "126.0"], ["estonia", "1.4", "0.3%", "45226", "1.0%", "29.0"], ["finland", "5.3", "1.1%", "337030", "7.6%", "16.0"], ["france", "65.03", "13.%", "643548", "14.6%", "111.0"], ["germany", "80.4", "16.6%", "357021", "8.1%", "225.0"], ["greece", "11.1", "2.2%", "131940", "3.0%", "84.0"], ["hungary", "10.1", "2.0%", "93030", "2.1%", "108.0"], ["ireland", "4.2", "0.8%", "70280", "1.6%", "60.0"], ["italy", "58.8", "11.9%", "301320", "6.8%", "195.0"], ["latvia", "2.3", "0.5%", "64589", "1.5%", "35.0"], ["lithuania", "3.4", "0.7%", "65200", "1.5%", "52.0"], ["luxembourg", "0.5", "0.1%", "2586", "0.1%", "181.0"], ["malta", "0.4", "0.1%", "316", "0.0%", "1261.0"], ["netherlands", "16.4", "3.3%", "41526", "0.9%", "394.0"], ["poland", "38.1", "7.7%", "312685", "7.1%", "122.0"], ["portugal", "10.6", "2.1%", "92931", "2.1%", "114.0"], ["romania", "21.6", "4.4%", "238391", "5.4%", "91.0"], ["spain", "44.7", "9.0%", "504782", "11.4%", "87.0"], ["slovakia", "5.4", "1.1%", "48845", "1.1%", "111.0"], ["slovenia", "2.0", "0.4%", "20253", "0.5%", "99.0"], ["sweden", "9.1", "1.8%", "449964", "10.2%", "20.0"]]}, "native_split": "test", "question": "Which factors in the table, such as 'area km 2', 'area % of eu', or 'pop density people / km 2', significantly influence the 'population % of eu' for each member state? If none have an effect, please reply 'no clear impact'.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "area km 2, area % of eu", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3d4a4379fab0e72179a4a20199c27a18"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:82bd9265aea9a4af6071566d7664bc52", "source": "tablebench", "table_key": "ff38a284f881839688c29ac79374b58e24c14b89", "grid": {"TD": 1, "LD": 0, "nrows": 25, "ncols": 8, "section_rows": [], "cells": [["rank", "rank fortune 500", "name", "headquarters", "revenue (millions)", "profit (millions)", "employees", "industry"], ["1", "17", "sinopec", "beijing", "131636.0", "3703.1", "681900", "oil"], ["2", "24", "china national petroleum", "beijing", "110520.2", "13265.3", "1086966", "oil"], ["3", "29", "state grid corporation", "beijing", "107185.5", "2237.7", "1504000", "utilities"], ["4", "170", "industrial and commercial bank of china", "beijing", "36832.9", "6179.2", "351448", "banking"], ["5", "180", "china mobile limited", "beijing", "35913.7", "6259.7", "130637", "telecommunications"], ["6", "192", "china life insurance", "beijing", "33711.5", "173.9", "77660", "insurance"], ["7", "215", "bank of china", "beijing", "30750.8", "5372.3", "232632", "banking"], ["8", "230", "china construction bank", "beijing", "28532.3", "5810.3", "297506", "banking"], ["9", "237", "china southern power grid", "guangzhou", "27966.1", "1074.1", "178053", "utilities"], ["10", "275", "china telecom", "beijing", "24791.3", "2279.7", "400299", "telecommunications"], ["11", "277", "agricultural bank of china", "beijing", "24475.5", "728.4", "452464", "banking"], ["12", "290", "hutchison whampoa", "hong kong", "23661.0", "2578.3", "220000", "various sectors"], ["13", "299", "sinochem corporation", "beijing", "23109.2", "344.7", "20343", "various sectors"], ["14", "307", "baosteel", "shanghai", "22663.4", "1622.2", "91308", "steel"], ["15", "342", "china railway engineering", "beijing", "20520.4", "142.6", "275866", "railway"], ["16", "384", "china railway construction", "beijing", "18735.7", "70.2", "245540", "railway"], ["17", "385", "first automotive works", "changchun", "18710.7", "70.0", "136010", "automobile"], ["18", "396", "china state construction", "beijing", "18163.2", "281.3", "294309", "construction"], ["19", "402", "saic motor", "shanghai", "18010.1", "89.7", "72416", "automobile"], ["20", "405", "cofco limited", "beijing", "17953.2", "281.0", "82481", "various sectors"], ["21", "435", "china minmetals", "beijing", "16902.2", "154.4", "32594", "metal trading"], ["22", "457", "jardine matheson", "hong kong / hamilton", "16281.0", "1348.0", "240000", "various sectors"], ["23", "469", "china national offshore oil", "beijing", "16038.9", "3007.1", "44000", "oil"], ["24", "488", "china ocean shipping", "beijing", "15413.5", "1092.9", "79616", "shipping"]]}, "native_split": "test", "question": "What is the correlation between the 'revenue (millions)' and 'profit (millions)' columns in the table? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Weak positive correlation, 0.55", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "82bd9265aea9a4af6071566d7664bc52"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:e41eb9209114381d9de98dc4c249ed76", "source": "tablebench", "table_key": "e033aeee82f0161993874e861e7e6ca9ece0bdcf", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["type", "numbers", "year built", "quantity built", "power (horsepower)", "max speed (km / h)"], ["rhn", "1011 - 1048 (power cars) 11 - 48 (trailer cars)", "1967", "38 + 38", "220", "90"], ["rts", "d9 - d16 (power cars) ts4 - ts7 (center / trailer cars)", "1971", "8 + 4", "220", "70"], ["thn", "1101 - 1140", "1983", "40", "235", "105"], ["nkf", "1201 - 1264 , (center) 2101 - 2112", "1985", "64 + 12", "235", "105"], ["asr ( class 158 express sprinter )", "2501 - 2512 , (center) 2113 - 2120", "1991", "12 + 8", "285", "120"], ["apd 20", "2513 - 2524 (center) 2121 - 2128", "1995", "10 + 8", "298", "120"], ["apd 60", "2525 - 2544", "1996", "20 + 40", "298", "120"]]}, "native_split": "test", "question": "How does the increase in power (horsepower) impact the maximum speed (km / h) of the trains across different years?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Positive impact", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "e41eb9209114381d9de98dc4c249ed76"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:08fc43089f4ff11e549b34277c165afa", "source": "tablebench", "table_key": "a357a2f0b1eef59c32e827edd3460c8c16ae672f", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 13, "section_rows": [], "cells": [["Club", "Season", "League", "League", "League", "National Cup", "National Cup", "League Cup", "League Cup", "Europe", "Europe", "Total", "Total"], ["Club", "Season", "Division", "Apps", "Goals", "Apps", "Goals", "Apps", "Goals", "Apps", "Goals", "Apps", "Goals"], ["Liverpool", "1990–91", "First Division", "2", "0", "1", "0", "0", "0", "0", "0", "3", "0"], ["Liverpool", "1991–92", "First Division", "30", "5", "8", "3", "5", "3", "8", "0", "51", "11"], ["Liverpool", "1992–93", "Premier League", "31", "4", "1", "0", "5", "2", "3", "1", "40", "7"], ["Liverpool", "1993–94", "Premier League", "30", "2", "2", "0", "2", "0", "0", "0", "34", "2"], ["Liverpool", "1994–95", "Premier League", "40", "7", "7", "0", "8", "2", "0", "0", "55", "9"], ["Liverpool", "1995–96", "Premier League", "38", "6", "7", "2", "4", "1", "4", "1", "53", "10"], ["Liverpool", "1996–97", "Premier League", "37", "7", "2", "0", "4", "2", "8", "1", "51", "10"], ["Liverpool", "1997–98", "Premier League", "36", "11", "1", "0", "5", "0", "4", "1", "46", "12"], ["Liverpool", "1998–99", "Premier League", "28", "4", "0", "0", "0", "0", "3", "1", "31", "5"], ["Liverpool", "Liverpool Total", "Liverpool Total", "272", "46", "29", "5", "33", "10", "30", "5", "364", "66"], ["Real Madrid", "1999–2000", "La Liga", "30", "3", "10", "0", "0", "0", "7", "1", "47", "4"], ["Real Madrid", "2000–01", "La Liga", "26", "2", "6", "0", "0", "0", "10", "0", "42", "2"], ["Real Madrid", "2001–02", "La Liga", "23", "2", "2", "0", "0", "0", "13", "2", "38", "4"], ["Real Madrid", "2002–03", "La Liga", "15", "1", "4", "1", "0", "0", "6", "2", "25", "4"], ["Real Madrid", "Real Madrid Total", "Real Madrid Total", "94", "8", "22", "1", "0", "0", "36", "5", "152", "14"], ["Manchester City", "2003–04", "Premier League", "22", "0", "3", "0", "1", "0", "4", "0", "30", "0"], ["Manchester City", "2004–05", "Premier League", "13", "0", "1", "0", "0", "0", "0", "0", "14", "0"], ["Manchester City", "Manchester City Total", "Manchester City Total", "35", "0", "4", "0", "1", "0", "4", "0", "44", "0"], ["Career Total", "Career Total", "Career Total", "401", "54", "52", "6", "37", "10", "70", "10", "560", "80"]]}, "native_split": "test", "question": "Does an increase in the number of appearances ('Apps') causally influence the number of goals scored ('Goals') for a football player?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: Yes, Higher interest positively influences deposit balances change (correlation coefficient of 0.89).\",\nFinal Answer: No, Analysis reveals a negligible inverse correlation (0.21), suggesting the gdp does not causally influence the population.\nFinal Answer: The water level of a river exhibits a stronger causal relationship with rainfall (0.82) compared to snowfall (0.32).\n\nEnsure the final answer format is the last output line and the answer should give the conclusion then provide a brief explanation of the causal analysis results as concise as possible.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Yes, a strong positive correlation (0.97) indicates that an increase in appearances ('Apps') causally influences the number of goals scored ('Goals') for a football player.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "08fc43089f4ff11e549b34277c165afa"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:77ad2ce9d250a32132f06e3679f8fc49", "source": "tablebench", "table_key": "8d0733cd7011cce07b2af3aaba6b4161263dee8b", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 4, "section_rows": [], "cells": [["Particulars", "Total", "Male", "Female"], ["Total No. of Houses", "14", "-", "-"], ["Population", "55", "25", "30"], ["Child (0-6)", "7", "3", "4"], ["Schedule Caste", "1000", "500", "500"], ["Schedule Tribe", "0", "0", "0"], ["Literacy", "79.17 %", "86.36 %", "73.08 %"], ["Total Workers", "15", "12", "3"], ["Main Worker", "-10", "-5", "-5"], ["Marginal Worker", "0", "0", "0"]]}, "native_split": "test", "question": "What anomaly can be detected in the demographic data of a rural town, and what could be the possible explanations for these anomalies??", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies include an unusually high Schedule Caste count of 1000, possibly due to a data entry error or unique demographic trait, and a negative Main Worker value (-10).", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "77ad2ce9d250a32132f06e3679f8fc49"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:ebfb7f83b151375b826603749c259ed6", "source": "tablebench", "table_key": "a3408cab6ba9c0424f137969852ca18b250b26c9", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 5, "section_rows": [], "cells": [["lga name", "area (km 2 )", "census 2006 population", "administrative capital", "postal code"], ["port harcourt", "109", "541115", "port harcourt", "500"], ["obio - akpor", "260", "464789", "rumuodumaya", "500"], ["okrika", "222", "222026", "okrika", "500"], ["ogu / bolo", "89", "74683", "ogu", "500"], ["eleme", "138", "190884", "eleme", "501"], ["tai", "159", "117797", "sakpenwa", "501"], ["gokana", "126", "228828", "kpor", "501"], ["khana", "560", "294217", "bori", "502"], ["oyigbo", "248", "122687", "afam", "502"], ["opobo / nkoro", "130", "151511", "opobo town", "503"], ["andoni", "233", "211009", "ngo", "503"], ["bonny", "642", "215358", "bonny", "503"], ["degema", "1011", "249773", "degema", "504"], ["asari - toru", "113", "220100", "buguma", "504"], ["akuku - toru", "1443", "156006", "abonnema", "504"], ["abua / odual", "704", "282988", "abua", "510"], ["ahoada west", "403", "249425", "akinima", "510"], ["ahoada east", "341", "166747", "ahoada", "510"], ["ogba / egbema / ndoni", "969", "284010", "omuku", "510"], ["emohua", "831", "201901", "emohua", "511"], ["ikwerre", "655", "189726", "isiokpo", "511"], ["etche", "805", "249454", "okehi", "512"]]}, "native_split": "test", "question": "Which factors in the table, such as 'area (km 2 )', 'administrative capital', or 'postal code', significantly influence the 'census 2006 population' for each LGA? If none have an effect, please reply 'no clear impact'.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No clear impact", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ebfb7f83b151375b826603749c259ed6"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:4191e612ed285e221ecbe1019a191a1a", "source": "tablebench", "table_key": "50dc3ebc6e1544f6cb51a141add47fdcf70bae64", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 6, "section_rows": [], "cells": [["Animal", "Sex", "Metabolic rate", "Mean", "Difference from mean", "Squared difference from mean"], ["1", "Female", "727.7", "1285.5", "557.8", "311140.84"], ["2", "Female", "1086.5", "1285.5", "199.0", "39601.00"], ["3", "Female", "1091.0", "1285.5", "194.5", "37830.25"], ["4", "Female", "1361.3", "1285.5", "75.8", "5745.64"], ["5", "Female", "1490.5", "1285.5", "205.0", "42025.00"], ["6", "Female", "1956.1", "1285.5", "670.6", "449704.36"], ["-", "-", "-", "-", "-", "-"], ["Mean of metabolic rates", "Mean of metabolic rates", "Mean of metabolic rates", "1285.5", "Sum of squared differences", "886047.09"]]}, "native_split": "test", "question": "What is the standard deviation of the metabolic rates for female animals, and which animal has a metabolic rate that is more than one standard deviation away from the mean?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "420.96, 1, 6", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "4191e612ed285e221ecbe1019a191a1a"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:47219e7225da35f61cb5307288f2eac3", "source": "tablebench", "table_key": "c453ab8adc1cb0b206ed8275a8bbe30dcd9ad70e", "grid": {"TD": 1, "LD": 0, "nrows": 37, "ncols": 5, "section_rows": [], "cells": [["asian rank", "world rank", "country", "gdp per capita", "gdp world rank"], ["1", "1", "qatar", "85638", "69"], ["2", "4", "brunei", "50790", "113"], ["3", "5", "singapore", "49754", "44"], ["4", "9", "kuwait", "39344", "56"], ["5", "14", "united arab emirates", "37941", "55"], ["6", "22", "japan", "33596", "3"], ["7", "24", "bahrain", "31899", "105"], ["8", "26", "republic of china (taiwan)", "30322", "19"], ["9", "31", "israel", "27147", "52"], ["10", "34", "south korea", "24803", "14"], ["11", "36", "oman", "23987", "77"], ["12", "38", "saudi arabia", "22852", "22"], ["13", "59", "malaysia", "13385", "30"], ["14", "66", "lebanon", "11279", "84"], ["15", "71", "iran", "10570", "18"], ["16", "83", "thailand", "7907", "24"], ["17", "100", "people 's republic of china", "7325", "2"], ["18", "105", "jordan", "6976", "99"], ["19", "106", "bhutan", "6962", "n / a"], ["20", "109", "maldives", "4603", "n / a"], ["21", "111", "syria", "6892", "63"], ["22", "113", "sri lanka", "6765", "65"], ["23", "120", "indonesia", "6728", "16"], ["24", "122", "philippines", "3383", "37"], ["25", "124", "mongolia", "3222", "141"], ["26", "127", "pakistan", "2594", "26"], ["27", "128", "vietnam", "2589", "46"], ["28", "129", "india", "2563", "4"], ["29", "107", "east timor", "4770", "156"], ["30", "132", "yemen", "2343", "81"], ["31", "136", "laos", "2054", "128"], ["32", "140", "papua new guinea", "1974", "131"], ["33", "143", "cambodia", "1818", "103"], ["34", "153", "bangladesh", "1311", "48"], ["35", "159", "nepal", "3397", "96"], ["36", "161", "burma", "1040", "78"]]}, "native_split": "test", "question": "What is the median GDP per capita of the top 20 countries by world rank?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "24395", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "47219e7225da35f61cb5307288f2eac3"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:1501b744e926341ae4a8556e8d3805d0", "source": "tablebench", "table_key": "25c492ac52505fd03cc20c7621752c718fa2de6b", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 6, "section_rows": [], "cells": [["year", "property taxes", "investment earnings", "other local sources", "state & federal", "total revenue"], ["2005", "24384901", "255041", "2670060", "13581968", "40891700"], ["2004", "21099214", "181103", "2624131", "13999169", "37903617"], ["2003", "17199210", "509862", "2309087", "12794922", "32539572"], ["2002", "14359199", "879398", "2168096", "15132879", "32539572"], ["2001", "11631227", "1949885", "1987436", "12929489", "28498037"], ["2000", "10608734", "493839", "2127480", "8549565", "21779618"]]}, "native_split": "test", "question": "What trend can be observed in the total revenue of the entity over the years, and what might be the projected total revenue for the next year?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\n[Answer Example]\nFinal Answer: 1, 2, 3\nFinal Answer: Increasing trend\nFinal Answer: Increasing trend, 13.5\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number, entity name or a trend description(No clear trend, Increasing trend or Decreasing trend), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Increasing trend, 44736401", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "1501b744e926341ae4a8556e8d3805d0"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:32214d4bce9494dbaee113ab28f58dc1", "source": "tablebench", "table_key": "bb4700225b2d2b08fc8fd2696546e4a6b6288d9b", "grid": {"TD": 1, "LD": 0, "nrows": 26, "ncols": 8, "section_rows": [], "cells": [["Unnamed: 0", "Average population (x 1000)", "Live births", "Deaths", "Natural change", "Crude birth rate (per 1000)", "Crude death rate (per 1000)", "Natural change (per 1000)"], ["1970", "31", "683", "356", "327", "22.0", "11.5", "10.5"], ["1975", "33", "706", "374", "332", "21.4", "11.3", "10.1"], ["1980", "35", "701", "351", "350", "20.0", "10.0", "10.0"], ["1985", "37", "793", "289", "504", "21.4", "7.8", "13.6"], ["1990", "38", "635", "342", "293", "16.9", "9.1", "7.8"], ["1991", "38", "623", "350", "273", "16.6", "9.3", "7.3"], ["1992", "37", "611", "369", "242", "16.7", "10.1", "6.6"], ["1993", "34", "459", "433", "26", "13.3", "12.6", "0.8"], ["1994", "32", "433", "460", "- 27", "13.5", "14.3", "-0.8"], ["1995", "31", "382", "481", "- 99", "12.5", "15.8", "-3.2"], ["1996", "29", "374", "436", "- 62", "12.7", "14.8", "-2.1"], ["1997", "29", "373", "400", "- 27", "13.0", "13.9", "-0.9"], ["1998", "28", "396", "355", "41", "14.2", "12.7", "1.5"], ["1999", "27", "319", "397", "- 78", "11.8", "14.7", "-2.9"], ["2000", "26", "289", "391", "- 102", "11.0", "14.9", "-3.9"], ["2001", "26", "298", "390", "- 92", "11.6", "15.1", "-3.6"], ["2002", "25", "310", "376", "- 66", "12.3", "14.9", "-2.6"], ["2003", "24", "268", "462", "- 194", "11.0", "19.0", "-8.0"], ["2004", "24", "339", "463", "- 124", "14.4", "19.7", "-5.3"], ["2005", "23", "294", "466", "- 172", "12.9", "20.5", "-7.6"], ["2006", "22", "270", "366", "- 96", "12.3", "16.7", "-4.4"], ["2007", "21", "280", "351", "- 71", "13.2", "16.5", "-3.3"], ["2008", "20", "267", "368", "- 101", "13.0", "18.0", "-4.9"], ["2009", "20", "268", "365", "- 97", "13.6", "18.5", "-4.9"], ["2010", "19", "233", "397", "- 164", "12.3", "20.9", "-8.7"]]}, "native_split": "test", "question": "Does a higher crude birth rate causally influence the natural change in population?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: Yes, Higher interest positively influences deposit balances change (correlation coefficient of 0.89).\",\nFinal Answer: No, Analysis reveals a negligible inverse correlation (0.21), suggesting the gdp does not causally influence the population.\nFinal Answer: The water level of a river exhibits a stronger causal relationship with rainfall (0.82) compared to snowfall (0.32).\n\nEnsure the final answer format is the last output line and the answer should give the conclusion then provide a brief explanation of the causal analysis results as concise as possible.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Yes, higher crude birth rate positively influences natural population change (correlation coefficient of 0.63).", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "32214d4bce9494dbaee113ab28f58dc1"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:084783c5325f5e96c17f174c4bf121d9", "source": "tablebench", "table_key": "131084196d5fd0d8ce0e3508e26415055ac15b8b", "grid": {"TD": 1, "LD": 0, "nrows": 45, "ncols": 4, "section_rows": [], "cells": [["Bank", "Foundation", "# of Branches\nAs of 30 September 2012", "Total Assets (million TL)\nAs of 30 September 2012"], ["Türkiye İş Bankası", "1924", "1,294", "210,535"], ["Ziraat Bankası", "1863", "1,510", "207,871"], ["Garanti Bank", "1946", "947", "154,550"], ["Akbank", "1948", "963", "150,241"], ["Yapı ve Kredi Bankası", "1944", "949", "160,309"], ["Halk Bankası", "1938", "807", "116,372"], ["VakıfBank", "1954", "741", "135,578"], ["Finansbank", "1987", "530", "49,902"], ["Türk Ekonomi Bankası", "1927", "510", "42,505"], ["Denizbank", "1997", "624", "40,457"], ["HSBC Bank", "1990", "331", "25,797"], ["ING Bank", "1984", "320", "23,184"], ["Türk Eximbank", "1987", "2", "14,724"], ["Şekerbank", "1953", "272", "14,656"], ["İller Bankası", "1933", "19", "12,309"], ["Türkiye Sınai Kalkınma Bankası", "1950", "4", "9,929"], ["Alternatif Bank", "1992", "63", "7,904"], ["Citibank", "1980", "37", "7,884"], ["Anadolubank", "1996", "88", "7,218"], ["Burgan Bank", "1992", "60", "4,275"], ["İMKB Takas ve Saklama Bankası", "1995", "1", "3,587"], ["Tekstilbank", "1986", "44", "3,502"], ["Deutsche Bank", "1988", "1", "3,426"], ["Fibabanka", "1984", "27", "3,120"], ["Aktif Yatırım Bankası", "1999", "7", "2,997"], ["The Royal Bank of Scotland", "1921", "3", "2,750"], ["Türkiye Kalkınma Bankası", "1975", "1", "2,651"], ["Turkland Bank", "1991", "27", "2,649"], ["Arap Türk Bankası", "1977", "7", "2,147"], ["Merrill Lynch", "1992", "1", "1,898"], ["BankPozitif", "1999", "1", "1,788"], ["Société Générale", "1989", "16", "1,457"], ["Turkish Bank", "1982", "20", "837"], ["JPMorgan Chase", "1984", "1", "830"], ["Birleşik Fon Bankası", "1958", "1", "801"], ["Bank Mellat", "1982", "3", "729"], ["Portigon", "1985", "1", "279"], ["Nurol Yatırım Bankası", "1999", "2", "227"], ["Diler Yatırım Bankası", "1998", "1", "108"], ["GSD Yatırım Bankası", "1998", "1", "108"], ["Habib Bank Limited", "1983", "1", "80"], ["Credit Agricole", "1990", "1", "72"], ["Adabank", "1985", "1", "51"], ["Taib Yatırım Bank", "1987", "1", "18"]]}, "native_split": "test", "question": "How does the number of branches impact the total assets of a bank?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Positive impact", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "084783c5325f5e96c17f174c4bf121d9"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:cd7a0470a94744dea67c879191f97bcd", "source": "tablebench", "table_key": "3f75a926d2b49e8a500c7a5b4cb03f896dd5ae16", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 9, "section_rows": [], "cells": [["language", "sorata municipality", "guanay municipality", "tacacoma municipality", "quiabaya municipality", "combaya municipality", "tipuani municipality", "mapiri municipality", "teoponte municipality"], ["quechua", "363.0", "1.653", "1.058", "33.0", "20.0", "1.587", "3.649", "756.0"], ["aymara", "16.029", "3.405", "4.389", "2.269", "2.522", "2.534", "1.767", "2.837"], ["guaranã­", "7000.0", "5.0", "1.0", "0.0", "0.0", "20.0", "6.0", "6.0"], ["another native", "8.0", "94.0", "17.0", "2.0", "1.0", "18.0", "7.0", "22.0"], ["spanish", "11.223", "10000.0", "4.321", "1.391", "1.214", "8.594", "8.567", "6.211"], ["foreign", "70.0", "86.0", "6.0", "6.0", "1.0", "61.0", "17.0", "33.0"], ["only native", "6.68", "737.0", "1.599", "1.023", "1.363", "190.0", "363.0", "472.0"], ["native and spanish", "9.54", "4.123", "3.389", "1.256", "1.162", "3.499", "4.653", "2.925"]]}, "native_split": "test", "question": "What anomalies can be identified in the language distribution data across different municipalities?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies are the excessively high values for 'sorata municipality' (7000.0) and 'guanay municipality' (10000.0) in their respective rows", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "cd7a0470a94744dea67c879191f97bcd"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:3b8b776fc1eab9d55aa640975266e11f", "source": "tablebench", "table_key": "e1dc7098f5d11ebe44e354eca6302ee697815ee1", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 10, "section_rows": [], "cells": [["series", "presenters", "start date", "end date", "days in camp", "camp mates", "winner", "highest viewers (millions)", "lowest viewers (millions)", "average viewers (millions)"], ["one", "ant & dec", "25 august 2002", "8 september 2002", "15", "8", "tony blackburn", "10.95", "6.14", "7.58"], ["two", "ant & dec", "28 april 2003", "12 may 2003", "15", "10", "phil tufnell", "12.75", "5.15", "8.55"], ["three", "ant & dec", "26 january 2004", "9 february 2004", "16", "10", "kerry katona", "14.99", "8.96", "11.02"], ["four", "ant & dec", "21 november 2004", "6 december 2004", "18", "11", "joe pasquale", "11.43", "7.04", "8.66"], ["five", "ant & dec", "20 november 2005", "5 december 2005", "18", "12", "carol thatcher", "12.35", "7.69", "9.42"], ["six", "ant & dec", "13 november 2006", "1 december 2006", "19", "12", "matt willis", "10.05", "6.97", "8.01"], ["seven", "ant & dec", "12 november 2007", "30 november 2007", "20", "11", "christopher biggins", "8.84", "5.0", "7.34"], ["eight", "ant & dec", "16 november 2008", "5 december 2008", "21", "12", "joe swash", "10.19", "7.91", "8.78"], ["nine", "ant & dec", "15 november 2009", "4 december 2009", "21", "13", "gino d'acampo", "10.86", "7.86", "9.37"], ["ten", "ant & dec", "14 november 2010", "4 december 2010", "21", "13", "stacey solomon", "13.48", "6.68", "9.7"], ["eleven", "ant & dec", "13 november 2011", "3 december 2011", "21", "13", "dougie poynter", "11.8", "6.8", "9.74"], ["twelve", "ant & dec", "11 november 2012", "1 december 2012", "21", "12", "charlie brooks", "11.51", "7.81", "9.81"]]}, "native_split": "test", "question": "Can you provide a detailed description of the table, including the main columns and any notable trends or patterns observed in the data?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The table presents the shooting accuracy of 8 different bullet types (including .308 Winchester and .300 Winchester Magnum) at 100 meters and 300 meters, measured in millimeters and Minutes of Angle (MOA) for dispersion. The data indicates that .300 Winchester Magnum bullets exhibit higher precision at 300 meters, with smaller dispersion ranges.\n\nEnsure the final answer format is the last output line and the answer should give a brief description of the table and provide descriptive explanations for the main columns and offer some basic insights about the table.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The table provides comprehensive data on the various seasons of the television series hosted by \"Ant & Dec,\" encompassing details such as season duration, participant count, winners, and viewership metrics. The data illustrates trends, including a rise in both season length and participant numbers across the seasons, coupled with consistently high viewership figures, which underscore the series' enduring popularity.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3b8b776fc1eab9d55aa640975266e11f"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:9bf461bb97059a48873ba437a8f4e6f7", "source": "tablebench", "table_key": "7ff5e94b58f555af78b8b942ff16b3438738e63e", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["year", "typhus", "typhoid fever", "relapsing fever", "smallpox", "malaria"], ["1913", "120", "424", "30", "67", "3600"], ["191822", "1300", "293", "639", "106", "2940 (avg)"], ["1929", "40", "170", "6", "8", "3000"], ["1930", "60", "190", "5", "10", "2700"], ["1931", "80", "260", "4", "30", "3200"], ["1932", "220", "300", "12", "80", "4500"], ["1933", "800", "210", "12", "38", "6500"], ["1934", "410", "200", "10", "16", "9477"], ["1935", "120", "140", "6", "4", "9924"]]}, "native_split": "test", "question": "Can you describe the main features of the table, including the key columns?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The table presents the shooting accuracy of 8 different bullet types (including .308 Winchester and .300 Winchester Magnum) at 100 meters and 300 meters, measured in millimeters and Minutes of Angle (MOA) for dispersion. The data indicates that .300 Winchester Magnum bullets exhibit higher precision at 300 meters, with smaller dispersion ranges.\n\nEnsure the final answer format is the last output line and the answer should give a brief description of the table and provide descriptive explanations for the main columns and offer some basic insights about the table.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The table provides data on the incidence of five infectious diseases—typhus, typhoid fever, relapsing fever, smallpox, and malaria—from 1913 to 1935. It includes columns for each disease, detailing the annual number of reported cases.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9bf461bb97059a48873ba437a8f4e6f7"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:558a7154502318571bf00b0fa0773817", "source": "tablebench", "table_key": "9c7b7bceddcff67fa5ba862c95249d5f5e257968", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 10, "section_rows": [], "cells": [["term abroad", "2000 / 01", "2001 / 02", "2002 / 03", "2003 / 04", "2004 / 05", "2005 / 06", "2006 / 07", "2007 / 08", "2008 / 09"], ["summer term", "33.7", "34.4", "32.7", "37.0", "37.2", "37.2", "38.7", "38.1", "35.8"], ["one semester", "38.5", "39.0", "40.3", "38.1", "37.5", "36.9", "36.3", "35.5", "37.3"], ["8 weeks or less during academic year", "7.4", "7.3", "9.4", "8.9", "8.0", "9.5", "9.8", "11.0", "11.7"], ["january term", "7.0", "6.0", "50.0", "5.7", "6.0", "5.4", "6.8", "7.2", "7.0"], ["academic year", "7.3", "7.8", "6.7", "6.0", "6.0", "5.3", "4.3", "4.1", "4.1"], ["one quarter", "4.1", "3.9", "3.8", "3.3", "3.3", "3.3", "3.4", "3.4", "3.3"], ["two quarters", "0.6", "0.5", "0.4", "0.5", "1.3", "0.9", "0.5", "0.6", "100.0"]]}, "native_split": "test", "question": "Can you identify which data points in the table deviate significantly from the norm?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies are the unusually high value of 50.0 in the '2002 / 03' column for 'january term' and the excessively large figure of 100.0 in the '2008 / 09' column for 'two quarters'.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "558a7154502318571bf00b0fa0773817"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:3dd80c36b2f40f02460ce053d30afa0a", "source": "tablebench", "table_key": "6e9203caf5494fc1db598fbd25855b47bdabdc4a", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 6, "section_rows": [], "cells": [["draw", "language", "artist", "song", "place", "points"], ["1", "icelandic", "beathoven", "þú og þeir (sókrates)", "16", "20"], ["2", "swedish", "tommy körberg", "stad i ljus", "12", "52"], ["3", "finnish", "boulevard", "nauravat silmät muistetaan", "20", "3"], ["4", "english", "scott fitzgerald", "go", "2", "136"], ["5", "turkish", "mfö", "sufi", "15", "37"], ["6", "spanish", "la década prodigiosa", "la chica que yo quiero (made in spain)", "11", "58"], ["7", "dutch", "gerard joling", "shangri - la", "9", "70"], ["8", "hebrew", "yardena arazi", "ben adam (בן אדם)", "7", "85"], ["9", "french", "céline dion", "ne partez pas sans moi", "1", "137"], ["10", "english", "jump the gun", "take him home", "8", "79"], ["11", "german", "maxi & chris garden", "lied für einen freund", "14", "48"], ["12", "german", "wilfried", "lisa mona lisa", "21", "0"], ["13", "danish", "hot eyes", "ka' du se hva' jeg sa'", "3", "92"], ["14", "greek", "afroditi frida", "clown (κλόουν)", "17", "10"], ["15", "norwegian", "karoline krüger", "for vår jord", "5", "88"], ["16", "french", "reynaert", "laissez briller le soleil", "18", "5"], ["17", "french", "lara fabian", "croire", "4", "90"], ["18", "italian", "luca barbarossa", "vivo (ti scrivo)", "12", "52"], ["19", "french", "gérard lenorman", "chanteur de charme", "10", "64"], ["20", "portuguese", "dora", "voltarei", "18", "5"], ["21", "croatian", "srebrna krila", "mangup", "6", "87"]]}, "native_split": "test", "question": "What is the correlation coefficient between the 'draw' and 'points' columns?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "-0.02", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3dd80c36b2f40f02460ce053d30afa0a"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:db49e98ce73a3521cedba851f6fdc6de", "source": "tablebench", "table_key": "6bc46d1390ebb7ae114ec17a51e750a16af20f19", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 5, "section_rows": [], "cells": [["Province", "DC", "LV*", "PSI", "PCI"], ["Verona", "44.3", "10.8", "14.2", "11.5"], ["Vicenza", "49.1", "11.4", "10.1", "8.6"], ["Padua", "46.1", "6.4", "10.7", "16.3"], ["Treviso", "44.5", "7.8", "14.1", "12.1"], ["Belluno", "39.3", "7.0", "23.8", "13.1"], ["Venice", "31.7", "4.9", "15.9", "24.2"], ["Rovigo", "35.2", "3.3", "15.5", "29.0"], ["Veneto", "42.3", "7.8", "13.7", "15.5"]]}, "native_split": "test", "question": "What is the correlation between the 'DC' and 'PCI' values across different provinces in the Veneto region? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Strong negative correlation, -0.84", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "db49e98ce73a3521cedba851f6fdc6de"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:faaef2c1a2e22fbcb12eb4f7176d0493", "source": "tablebench", "table_key": "d43d7d54fd2f772a798c37f26f1ca65842d332fe", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 7, "section_rows": [], "cells": [["rank", "couple", "judges", "public", "total", "vote percentage", "result"], ["1", "hayley and daniel", "7", "6", "13", "25.1%", "safe"], ["2", "mikey and melanie", "3", "7", "10", "35.7%", "safe"], ["3", "gary and maria", "5", "5", "10", "13.5%", "safe"], ["4", "danny and frankie", "6", "3", "9", "6.2%", "safe"], ["5", "kieron and brianne", "4", "4", "8", "9.0%", "safe"], ["6", "danniella and matthew", "6", "1", "7", "4.2%", "bottom two"]]}, "native_split": "test", "question": "Which factors in the table, such as 'judges', 'public', and 'vote percentage', contribute most to the 'result' of each couple in the competition? If none have an effect, please reply 'no clear impact'.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "public", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "faaef2c1a2e22fbcb12eb4f7176d0493"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:706164d587335e3377a8a46268677aee", "source": "tablebench", "table_key": "93076ad416e6ae5d0f0dff8f53babee9c5d2390b", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 4, "section_rows": [], "cells": [["Particulars", "Total", "Male", "Female"], ["Total No. of Houses", "122", "-", "-"], ["Population", "524", "261", "263"], ["Child (0-6)", "95", "46", "49"], ["Schedule Caste", "275", "137", "138"], ["Schedule Tribe", "0", "0", "0"], ["Literacy", "60.14 %", "150 %", "55.14 %"], ["Total Workers", "194", "143", "1000"], ["Main Worker", "194", "0", "0"], ["Marginal Worker", "0", "0", "0"]]}, "native_split": "test", "question": "Can you identify any demographic categories within the table whose values significantly deviate from the expected patterns?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies include the 'Male' literacy rate at 150%, which exceeds the maximum possible value of 100%, and the 'Female' total workers count at 1000, an outlier compared to other values.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "706164d587335e3377a8a46268677aee"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:b5662b29ddce625847c130e673373add", "source": "tablebench", "table_key": "44b315a5474ffe62c32a48f1f1d37b07a0c523db", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 6, "section_rows": [], "cells": [["election", "candidates fielded", "of seats won", "total votes", "% of popular vote", "place"], ["1983", "4", "0", "3078", "0.19%", "7th"], ["1986", "9", "0", "4660", "0.24%", "5th"], ["1991", "42", "0", "12650", "0.86%", "4th"], ["1996", "71", "0", "31511", "1.99%", "5th"], ["2001", "72", "0", "197231", "12.39%", "3rd"], ["2005", "79", "0", "161842", "9.17%", "3rd"], ["2009", "85", "0", "134570", "8.21%", "3rd"], ["2013", "61", "1", "146607", "8.13%", "3rd"]]}, "native_split": "test", "question": "Can you provide a detailed description of the table, including explanations for each main column and some initial insights about the data?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The table presents the shooting accuracy of 8 different bullet types (including .308 Winchester and .300 Winchester Magnum) at 100 meters and 300 meters, measured in millimeters and Minutes of Angle (MOA) for dispersion. The data indicates that .300 Winchester Magnum bullets exhibit higher precision at 300 meters, with smaller dispersion ranges.\n\nEnsure the final answer format is the last output line and the answer should give a brief description of the table and provide descriptive explanations for the main columns and offer some basic insights about the table.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The table details the electoral performance of a political entity across various elections spanning from 1983 to 2013. It includes data on the number of candidates fielded, seats secured, total votes garnered, the percentage of the popular vote, and electoral positioning. The data reveals a progressive trend of escalating political influence, culminating in the entity's inaugural seat victory in 2013.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b5662b29ddce625847c130e673373add"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:243a25da2e37282f9cdf151f453b167d", "source": "tablebench", "table_key": "4da4639e1ddb41179ff7bbde5e46cd57312dfa7c", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["rank by average", "competition finish", "couple", "total", "number of dances", "average"], ["1", "2", "darren & lana", "374", "11", "34.0"], ["2", "1", "darrien & hollie", "356", "11", "32.4"], ["3", "3", "ben & stephanie", "342", "11", "31.1"], ["4", "6", "paul & pamela", "150", "5", "30.0"], ["5", "8", "stewart & clare", "80", "3", "26.7"], ["6", "5", "mark & jennifer", "169", "7", "24.1"], ["7", "10", "clive & helga", "24", "1", "24.0"], ["8", "4", "jp & stacey", "206", "9", "22.9"], ["9", "9", "rob & dawn", "45", "2", "22.5"]]}, "native_split": "test", "question": "Can you describe the content of the table, explain the significance of each main column, and provide some initial insights into the data?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The table presents the shooting accuracy of 8 different bullet types (including .308 Winchester and .300 Winchester Magnum) at 100 meters and 300 meters, measured in millimeters and Minutes of Angle (MOA) for dispersion. The data indicates that .300 Winchester Magnum bullets exhibit higher precision at 300 meters, with smaller dispersion ranges.\n\nEnsure the final answer format is the last output line and the answer should give a brief description of the table and provide descriptive explanations for the main columns and offer some basic insights about the table.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The table displays the results of a dance competition, categorizing couples by their average score per dance, overall competition placement, total scores, and the number of dances executed. It emphasizes variations in rankings based on total points compared to average performance, providing insights into consistency and overall success in the competition.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "243a25da2e37282f9cdf151f453b167d"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:b3a226be0844234bc84a3b9e67479eb3", "source": "tablebench", "table_key": "515d8584f677878f2657972e75053c02c596a95c", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 6, "section_rows": [], "cells": [["-", "2017", "2016", "2015", "2014", "2013"], ["Applications", "41,000", "42,335", "39,670", "37,280", "33,825"], ["Offer Rate (%)", "89.7", "86.4", "90.8", "88.6", "86.8"], ["Enrols", "6,065", "5,960", "5,810", "5,820", "5,505"], ["Yield (%)", "16.5", "16.3", "16.1", "17.6", "18.7"], ["Applicant/Enrolled Ratio", "6.76", "7.10", "6.83", "6.41", "6.14"], ["Average Entry Tariff", "n/a", "176", "471", "466", "463"]]}, "native_split": "test", "question": "What is the correlation between the number of applications and the offer rate from 2013 to 2017, and provide the correlation coefficient as evidence?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No correlation, 0.22", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b3a226be0844234bc84a3b9e67479eb3"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:48c12564a70819def0e4e80ce8e55649", "source": "tablebench", "table_key": "614e6eb9327d423f41a23a20cb335acbab68050d", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 5, "section_rows": [], "cells": [["producer", "product", "samples taken", "samples failed", "melamine content (mg / kg)"], ["shijiazhuang sanlu group", "三鹿牌嬰幼兒配方乳粉", "11", "11", "2563.0"], ["shanghai panda dairy", "熊貓可寶牌嬰幼兒配方乳粉", "5", "3", "619.0"], ["qingdao shengyuan dairy", "聖元牌嬰幼兒配方乳粉", "17", "8", "150.0"], ["shanxi gu cheng dairy", "古城牌嬰幼兒配方乳粉", "13", "4", "141.6"], ["jiangxi guangming yingxiong dairy", "英雄牌嬰幼兒配方乳粉", "2", "2", "98.6"], ["baoji huimin dairy", "惠民牌嬰幼兒配方乳粉", "1", "1", "79.17"], ["inner mongolia mengniu dairy", "蒙牛牌嬰幼兒配方乳粉", "28", "3", "68.2"], ["torador dairy industry (tianjin)", "可淇牌嬰幼兒配方乳粉", "1", "1", "67.94"], ["guangdong yashili group", "雅士利牌嬰幼兒配方乳粉", "30", "8", "53.4"], ["hunan peiyi dairy", "南山倍益牌嬰幼兒配方乳粉", "3", "1", "53.4"], ["heilongjiang qilin dairy", "嬰幼兒配方乳粉2段基粉", "1", "1", "31.74"], ["shanxi yashili dairy", "雅士利牌嬰幼兒配方乳粉", "4", "2", "26.3"], ["shenzhen jinbishi milk", "金必氏牌嬰幼兒配方乳粉", "2", "2", "18.0"], ["scient (guangzhou) infant nutrition", "施恩牌嬰幼兒配方乳粉", "20", "14", "17.0"], ["guangzhou jinding dairy products factory", "金鼎牌嬰幼兒配方乳粉", "3", "1", "16.2"], ["inner mongolia yili industrial group", "伊利牌兒童配方乳粉", "35", "1", "12.0"], ["yantai ausmeadow nutriment", "澳美多牌嬰幼兒配方乳粉", "16", "6", "10.7"], ["qingdao suncare nutritional technology", "愛可丁牌嬰幼兒配方乳粉", "3", "1", "4.8"], ["xi'an baiyue dairy", "御寶牌嬰幼兒配方乳粉", "3", "1", "3.73"], ["yantai leilei dairy", "磊磊牌嬰幼兒配方乳粉", "3", "3", "1.2"], ["shanghai baoanli dairy", "寶安力牌嬰幼兒配方乳粉", "1", "1", "0.21"], ["fuding chenguan dairy", "聰爾壯牌嬰幼兒配方乳粉", "1", "1", "0.09"]]}, "native_split": "test", "question": "What is the average melamine content (mg / kg) of the dairy products that had at least 5 samples taken?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "403.88", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "48c12564a70819def0e4e80ce8e55649"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:f08b94c7f4b830fd8c090b2ef668f701", "source": "tablebench", "table_key": "dcdbd3685b7177db90771a67d5b1671a96b0f22c", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 10, "section_rows": [], "cells": [["rank", "city", "province", "date of official foundation of municipality", "2006", "1996", "1986", "1976", "1966", "1956"], ["1", "tehran", "tehran", "1885", "7705036", "6758845", "6042584", "4530223", "2719730", "1512082"], ["2", "mashhad", "razavi khorasan", "1918", "2410800", "1887405", "1463508", "667770", "409616", "241984"], ["3", "esfahān", "esfahān", "1928", "1602110", "1266072", "986753", "661510", "424045", "254708"], ["4", "tabriz", "east azarbaijan", "1917", "1398060", "1191043", "971482", "597976", "403413", "289996"], ["5", "karaj", "alborz", "1934", "1377450", "940968", "611510", "137926", "44243", "14526"], ["6", "shiraz", "fars", "1950", "1227311", "1053025", "848289", "425813", "269865", "170659"]]}, "native_split": "test", "question": "Based on the historical population growth from 1956 to 2006, what could be the projected population of Tabriz in 2026?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\n[Answer Example]\nFinal Answer: 1, 2, 3\nFinal Answer: Increasing trend\nFinal Answer: Increasing trend, 13.5\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number, entity name or a trend description(No clear trend, Increasing trend or Decreasing trend), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1872811", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f08b94c7f4b830fd8c090b2ef668f701"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:0bee2233eca7e83249d4dc7f850f47f8", "source": "tablebench", "table_key": "0c295d92bd3766aae952efd9814751a3e68ca265", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["State", "Swimsuit", "Interview", "Evening Gown", "Average", "Finalists"], ["Iowa", "9.267", "9.651", "9.62", "9.513", "9.8"], ["Oklahoma", "9.226", "9.309", "9.487", "9.341", "9.586"], ["Kansas", "9.221", "9.6", "9.6", "9.474", "9.486"], ["Alabama", "9.01", "9.326", "9.449", "9.262", "9.471"], ["North Carolina", "9.079", "9.207", "9.5", "9.262", "9.414"], ["California", "9.34", "9.514", "9.486", "9.447", "9.4"], ["Rhode Island", "9.087", "9.279", "9.341", "9.235", "None"], ["Maryland", "9.021", "9.206", "9.271", "9.166", "None"], ["Indiana", "8.966", "9.103", "9.37", "9.146", "None"], ["Virginia", "8.984", "9.112", "9.279", "9.125", "None"], ["Mississippi", "8.917", "9.2", "9.247", "9.121", "None"], ["Illinois", "8.897", "8.969", "9.286", "9.05", "None"]]}, "native_split": "test", "question": "Which factors among 'Swimsuit', 'Interview', and 'Evening Gown' scores in the table significantly influence the 'Finalists' scores for each state? If none have an effect, please reply 'no clear impact'.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Evening Gown", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0bee2233eca7e83249d4dc7f850f47f8"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:502645e77ad3f4a83adb38da6c6168b7", "source": "tablebench", "table_key": "ecdc79ddd8af00f59c1ebd4537cef1689723feb0", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 9, "section_rows": [], "cells": [["rank", "airport", "total passengers", "% change 2007 / 2008", "international passengers", "domestic passengers", "transit passengers", "aircraft movements", "freight ( metric tonnes )"], ["1", "london heathrow", "67054745", "1.5%", "61344438", "5562516", "147791", "478693", "1397054"], ["2", "london gatwick", "34205887", "2.9%", "30431051", "3730963", "43873", "263653", "107702"], ["3", "london stansted", "22360364", "6.0%", "19996947", "2343428", "19989", "193282", "197738"], ["4", "manchester", "21219195", "4.0%", "18119230", "2943719", "156246", "204610", "141781"], ["5", "london luton", "10180734", "2.6%", "8853224", "1320678", "6832", "117859", "40518"], ["6", "birmingham airport", "9627589", "4.3%", "8105162", "1471538", "50889", "112227", "12192"], ["7", "edinburgh", "9006702", "0.5%", "3711140", "5281038", "14524", "125550", "12418"], ["8", "glasgow international", "8178891", "7.0%", "3943139", "4192121", "43631", "100087", "3546"], ["9", "bristol", "6267114", "5.7%", "5057051", "1171605", "38458", "76517", "3"], ["10", "east midlands", "5620673", "3.8%", "4870184", "746094", "4395", "93038", "261507"], ["11", "liverpool", "5334152", "2.5%", "4514926", "814900", "4326", "84890", "3740"], ["12", "belfast international", "5262354", "0.2%", "2122844", "3099995", "39515", "77943", "36115"], ["13", "newcastle", "5039993", "10.8%", "3506681", "1509959", "23353", "72904", "1938"], ["14", "aberdeen", "3290920", "3.6%", "1470099", "1820137", "684", "119831", "4006"], ["15", "london city", "3260236", "12.0%", "2600731", "659494", "11", "94516", "0"], ["16", "leeds bradford", "2873321", "0.3%", "2282358", "578089", "12874", "61699", "334"], ["17", "belfast city", "2570742", "17.5%", "70516", "2500225", "1", "42990", "168"], ["18", "glasgow prestwick", "2415755", "0.3%", "1728020", "685999", "1736", "42708", "22966"], ["19", "cardiff", "1994892", "5.5%", "1565991", "412728", "16173", "37123", "1334"]]}, "native_split": "test", "question": "How does a change in international passengers impact the rank of an airport?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No clear impact", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "502645e77ad3f4a83adb38da6c6168b7"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:8fd2215bcdb2dd18a9960652f796be73", "source": "tablebench", "table_key": "ea4851ca258f8bf7443497ee7fcb0338b9317ad7", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 7, "section_rows": [], "cells": [["episode number", "title", "original airing", "timeslot", "viewers", "top 50 ranking", "scripted show ranking"], ["112", "nice is different than good", "february 15 , 2010", "8:35 pm - 9:30 pm", "479100", "12", "3"], ["113", "being alive)", "february 22 , 2010", "8:30 pm - 9:30 pm", "477080", "8", "1"], ["114", "never judge a lady by her lover", "march 1 , 2010", "8:30 pm - 9:30 pm", "447990", "9", "1"], ["115", "the god - why - don't - you - love - me blues", "march 8 , 2010", "8:30 pm - 9:30 pm", "471200", "14", "4"], ["116", "everybody ought to have a maid", "march 15 , 2010", "8:30 pm - 9:30 pm", "448490", "15", "5"], ["117", "don't walk on the grass", "march 22 , 2010", "8:30 pm - 9:30 pm", "2000000", "12", "4"], ["118", "careful the things you say", "march 29 , 2010", "8:30 pm - 9:30 pm", "413820", "13", "5"], ["119", "the coffee cup", "april 12 , 2010", "8:30 pm - 9:30 pm", "397830", "23", "8"], ["120", "would i think of suicide", "april 19 , 2010", "8:30 pm - 9:30 pm", "391220", "25", "9"], ["121", "boom crunch", "april 26 , 2010", "8:30 pm - 9:30 pm", "411880", "21", "8"], ["122", "if", "may 3 , 2010", "8:30 pm - 9:30 pm", "419020", "21", "9"], ["123", "you gotta get a gimmick", "may 10 , 2010", "8:30 pm - 9:30 pm", "429540", "19", "8"], ["124", "how about a friendly shrink", "may 17 , 2010", "8:30 pm - 9:30 pm", "375760", "28", "10"], ["125", "the glamorous life", "may 24 , 2010", "3:00 am - 4:00 am", "375620", "25", "6"], ["126", "lovely", "may 31 , 2010", "8:30 pm - 9:30 pm", "400730", "28", "11"], ["127", "the chase", "june 7 , 2010", "8:30 pm - 9:30 pm", "391340", "23", "11"], ["128", "chromolume no 7", "june 14 , 2010", "8:30 pm - 9:30 pm", "425620", "25", "10"], ["129", "my two young men", "june 21 , 2010", "8:30 pm - 9:30 pm", "392270", "24", "7"], ["130", "we all deserve to die", "june 28 , 2010", "8:30 pm - 9:30 pm", "444790", "21", "8"], ["131", "epiphany", "july 5 , 2010", "8:30 pm - 9:30 pm", "524440", "18", "4"], ["132", "a little night music", "july 12 , 2010", "8:30 pm - 9:30 pm", "464820", "19", "6"], ["133", "the ballad of booth", "july 19 , 2010", "8:30 pm - 9:30 pm", "447060", "19", "5"]]}, "native_split": "test", "question": "What unusual patterns or outliers can be identified in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies are episode 117: \"don't walk on the grass\" with an unusually high viewership of 2,000,000, significantly exceeding the average of 400,000-500,000, and episode 125: \"the glamorous life,\" airing at an unusual timeslot of 3:00 am - 4:00 am, deviating from the typical 8:30 pm - 9:30 pm slot.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8fd2215bcdb2dd18a9960652f796be73"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:f2f9df4e6d78b8ace8927690f4d05613", "source": "tablebench", "table_key": "99ce628ce4c1c8596d51f48fe611296742cc28dd", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 8, "section_rows": [], "cells": [["rank", "company", "headquarters", "industry", "sales (billion )", "profits (billion )", "assets (billion )", "market value (billion )"], ["1", "jpmorgan chase", "usa", "banking", "115.5", "17.4", "2117.6", "182.2"], ["2", "hsbc", "uk", "banking", "103.3", "13.3", "2467.9", "186.5"], ["3", "general electric", "usa", "conglomerate", "156.2", "11.6", "751.2", "216.2"], ["4", "exxonmobil", "usa", "oil and gas", "341.6", "30.5", "302.5", "407.2"], ["5", "royal dutch shell", "netherlands", "oil and gas", "369.1", "20.1", "317.2", "212.9"], ["6", "petrochina", "china", "oil and gas", "222.3", "21.2", "251.3", "320.8"], ["7", "industrial and commercial bank of china", "china", "banking", "69.2", "18.8", "1723.5", "239.5"], ["8", "berkshire hathaway", "usa", "conglomerate", "136.2", "13.0", "372.2", "211.0"], ["8", "petrobras", "brazil", "oil and gas", "121.3", "21.2", "313.2", "238.8"], ["10", "citigroup", "usa", "banking", "111.5", "10.6", "1913.9", "132.8"], ["11", "bnp paribas", "france", "banking", "130.4", "10.5", "2680.7", "88.0"], ["11", "wells fargo", "usa", "banking", "93.2", "12.4", "1258.1", "170.6"], ["13", "santander group", "spain", "banking", "109.7", "12.8", "1570.6", "94.7"], ["14", "at&t inc", "usa", "telecommunications", "124.3", "19.9", "268.5", "168.2"], ["15", "gazprom", "russia", "oil and gas", "98.7", "25.7", "275.9", "172.9"], ["16", "chevron", "usa", "oil and gas", "189.6", "19.0", "184.8", "200.6"], ["17", "china construction bank", "china", "banking", "58.2", "15.6", "1408.0", "224.8"], ["18", "walmart", "usa", "retailing", "421.8", "16.4", "180.7", "187.3"], ["19", "total", "france", "oil and gas", "188.1", "14.2", "192.8", "138.0"], ["20", "allianz", "germany", "insurance", "142.9", "6.7", "838.4", "62.7"]]}, "native_split": "test", "question": "What is the impact of a company's sales on its market value among the companies listed in the same industry?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No clear impact", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f2f9df4e6d78b8ace8927690f4d05613"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:94dc60bbaa47bd13e61daa090520bf51", "source": "tablebench", "table_key": "5277c89ed75695d4131c5384fb0e78dd10772a37", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 4, "section_rows": [], "cells": [["year", "population", "Catholics (based on registration by the church itself)", "Percentage (based on registration by the church itself)"], ["1970", "12,957,621", "5,320,000", "40.5"], ["1980", "14,091,014", "5,620,000", "39.5"], ["1990", "14,892,574", "5,560,000", "37.0"], ["1995", "15,424,122", "5,385,258", "34.8"], ["2000", "15,863,950", "5,060,413", "31.6"], ["2005", "16,305,526", "4,406,000", "27.0"], ["2010", "16,574,989", "4,166,000", "25.0"], ["2015", "16,900,726", "3,882,000", "22.9"], ["2016", "16,979,120", "3,832,000", "22.4"], ["2017", "17,081,057", "3,769,000", "21.9"]]}, "native_split": "test", "question": "Based on the trend of Catholic population and percentage from 1970 to 2017, forecast the likely number of Catholics and percentage of Catholics in the population in 2025.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\n[Answer Example]\nFinal Answer: 1, 2, 3\nFinal Answer: Increasing trend\nFinal Answer: Increasing trend, 13.5\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number, entity name or a trend description(No clear trend, Increasing trend or Decreasing trend), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "17854340, 19.08%", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "94dc60bbaa47bd13e61daa090520bf51"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:13d88bef442eaf8c1a03ae3aebbbe620", "source": "tablebench", "table_key": "7e7f292cc8b2d969dd77f9725030cc6bc1d862c2", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["rank", "metropolitan area", "country", "gdp (ppp) (2008) billions of us", "metropolitan population (2006) millions", "gdp (ppp) us per capita"], ["1", "mexico city", "mexico", "390", "21.2", "20300"], ["2", "são paulo", "brazil", "388", "18.61", "20800"], ["3", "buenos aires", "argentina", "362", "13.52", "28000"], ["4", "rio de janeiro", "brazil", "201", "11.62", "17300"], ["5", "bogotá", "colombia", "180", "8.55", "21050"], ["6", "santiago", "chile", "120", "5.7", "21050"], ["7", "brasilia", "brazil", "110", "3.48", "31600"], ["8", "lima", "peru", "109", "8.35", "13100"], ["9", "medellín", "colombia", "102", "3.58", "28500"], ["10", "guadalajara", "mexico", "81", "3.95", "20500"]]}, "native_split": "test", "question": "How does the GDP (PPP) per capita change with increasing metropolitan population in millions?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: Yes, Higher interest positively influences deposit balances change (correlation coefficient of 0.89).\",\nFinal Answer: No, Analysis reveals a negligible inverse correlation (0.21), suggesting the gdp does not causally influence the population.\nFinal Answer: The water level of a river exhibits a stronger causal relationship with rainfall (0.82) compared to snowfall (0.32).\n\nEnsure the final answer format is the last output line and the answer should give the conclusion then provide a brief explanation of the causal analysis results as concise as possible.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "GDP (PPP) per capita exhibits no causal effect (correlation coefficient of -0.29) with increasing metropolitan population in millions.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "13d88bef442eaf8c1a03ae3aebbbe620"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:4571da3300307735b9cf00e6c8061715", "source": "tablebench", "table_key": "62907cf21c74d1dda054ab3bfa885e6a7ddbffba", "grid": {"TD": 1, "LD": 0, "nrows": 31, "ncols": 7, "section_rows": [], "cells": [["component 1", "bp comp 1 (˚c)", "component 2", "bp comp 2 (˚c)", "bp azeo (˚c)", "% wt comp 1", "% wt comp 2"], ["acetaldehyde", "21.0", "diethyl ether", "34.6", "20.5", "76.0", "24.0"], ["acetaldehyde", "21.0", "n - butane", "- 0.5", "- 7.0", "16.0", "84.0"], ["acetamide", "222.0", "benzaldehyde", "179.5", "178.6", "6.5", "93.5"], ["acetamide", "222.0", "nitrobenzene", "210.9", "202.0", "24.0", "76.0"], ["acetamide", "222.0", "o - xylene", "144.1", "142.6", "11.0", "89.0"], ["acetonitrile", "82.0", "ethyl acetate", "77.15", "74.8", "23.0", "77.0"], ["acetonitrile", "82.0", "toluene", "110.6", "81.1", "25.0", "75.0"], ["acetylene", "- 86.6", "ethane", "- 88.3", "- 94.5", "40.7", "59.3"], ["aniline", "184.4", "o - cresol", "191.5", "191.3", "8.0", "92.0"], ["carbon disulfide", "46.2", "diethyl ether", "34.6", "34.4", "1.0", "99.0"], ["carbon disulfide", "46.2", "1 , 1 - dichloroethane", "57.2", "46.0", "94.0", "6.0"], ["carbon disulfide", "46.2", "methyl ethyl ketone", "79.6", "45.9", "84.7", "15.3"], ["carbon disulfide", "46.2", "ethyl acetate", "77.1", "46.1", "97.0", "3.0"], ["carbon disulfide", "46.2", "methyl acetate", "57.0", "40.2", "73.0", "27.0"], ["chloroform", "61.2", "methyl ethyl ketone", "79.6", "79.9", "17.0", "83.0"], ["chloroform", "61.2", "n - hexane", "68.7", "60.0", "72.0", "28.0"], ["carbon tetrachloride", "76.8", "methyl ethyl ketone", "79.9", "73.8", "71.0", "29.0"], ["carbon tetrachloride", "76.8", "ethylene dichloride", "84.0", "75.3", "78.0", "22.0"], ["carbon tetrachloride", "76.8", "ethyl acetate", "77.1", "74.8", "57.0", "43.0"], ["cyclohexane", "81.4", "ethyl acetate", "77.15", "72.8", "46.0", "54.0"], ["cyclohexane", "81.4", "ethyl nitrate", "88.7", "74.5", "64.0", "36.0"], ["diethyl ether", "34.6", "methyl formate", "31.50", "28.2", "44.0", "56.0"], ["diethyl ether", "34.6", "methylene chloride", "40", "40.8", "30.0", "70.0"], ["nitromethane", "101.0", "toluene", "110.8", "96.5", "55.0", "45.0"], ["tetrahydrofuran", "65.6", "chloroform", "61.2", "72.5", "34.5", "65.5"], ["tetrahydrofuran", "65.6", "n - hexane", "69", "63.0", "46.5", "53.5"], ["toluene", "110.63", "pyridine", "115.3", "110.2", "78.0", "22.0"], ["propylene glycol", "188.2", "aniline", "184.4", "179.5", "43.0", "57.0"], ["propylene glycol", "188.2", "o - xylene", "144.4", "135.8", "10.0", "90.0"], ["propylene glycol", "188.2", "toluene", "110.6", "110.5", "1.5", "98.5"]]}, "native_split": "test", "question": "What is the correlation between the 'bp comp 1 (˚C)' and '% wt comp 1' columns in the table? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Weak negative correlation, -0.45", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "4571da3300307735b9cf00e6c8061715"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:c5b41b1733a460472e3d1bc744be96d1", "source": "tablebench", "table_key": "6bae0fbf74ed9dbe7c745e57a2b0f7d97c688686", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 6, "section_rows": [], "cells": [["ballarat fl", "wins", "byes", "losses", "draws", "against"], ["sunbury", "16", "1", "1", "0", "1022"], ["melton south", "12", "2", "4", "0", "1191"], ["redan", "12", "2", "4", "0", "974"], ["lake wendouree", "12", "2", "4", "0", "1127"], ["daylesford", "11", "2", "5", "0", "1109"], ["darley", "11", "2", "5", "0", "1230"], ["ballarat", "5", "2", "11", "0", "1665"], ["melton", "4", "2", "12", "0", "1638"], ["sebastapol", "3", "1", "14", "0", "1802"], ["east point", "2", "1", "15", "0", "2090"], ["bacchus marsh", "2", "1", "15", "0", "2375"]]}, "native_split": "test", "question": "What is the mean and standard deviation of the `against` column?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1474.81, 470.08", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c5b41b1733a460472e3d1bc744be96d1"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:764095679b6ee3fc079e732afebe4b29", "source": "tablebench", "table_key": "9103721ab33f02adcebb14c4b2efcce6071ae438", "grid": {"TD": 1, "LD": 0, "nrows": 33, "ncols": 8, "section_rows": [], "cells": [["location", "founded", "type", "enrollment", "joined", "left", "nickname", "present conference"], ["tuscaloosa , alabama", "1831", "public", "33602", "1921", "1932", "crimson tide", "sec"], ["auburn , alabama", "1856", "public", "25469", "1921", "1932", "tigers", "sec"], ["clemson , south carolina", "1889", "public", "20768", "1921", "1953", "tigers", "acc"], ["charleston , south carolina", "1770", "private", "11320", "1998", "2013", "cougars", "caa"], ["durham , north carolina", "1838", "private", "14591", "1928", "1953", "blue devils", "acc"], ["greenville , north carolina", "1907", "public", "27386", "1964", "1976", "pirates", "c - usa ( american in 2014)"], ["johnson city , tennessee", "1911", "public", "15536", "1978", "2005", "buccaneers", "atlantic sun (a - sun) (re - joining socon in 2014)"], ["gainesville , florida", "1853", "public", "49913", "1922", "1932", "gators", "sec"], ["washington , dc", "1821", "private", "24531", "1936", "1970", "colonials", "atlantic 10 (a - 10)"], ["athens , georgia", "1785", "public", "34475", "1921", "1932", "bulldogs", "sec"], ["atlanta , georgia", "1885", "public", "21557", "1921", "1932", "yellow jackets", "acc"], ["lexington , kentucky", "1865", "public", "28094", "1921", "1932", "wildcats", "sec"], ["baton rouge , louisiana", "1860", "public", "30000", "1922", "1932", "tigers", "sec"], ["huntington , west virginia", "1837", "public", "13450", "1976", "1997", "thundering herd", "c - usa"], ["college park , maryland", "1856", "public", "37631", "1923", "1953", "terrapins", "acc ( big ten in 2014)"], ["oxford , mississippi", "1848", "public", "17142", "1922", "1932", "rebels", "sec"], ["starkville , mississippi", "1878", "public", "20424", "1921", "1932", "bulldogs", "sec"], ["chapel hill , north carolina", "1789", "public", "29390", "1921", "1953", "tar heels", "acc"], ["raleigh , north carolina", "1887", "public", "34767", "1921", "1953", "wolfpack", "acc"], ["richmond , virginia", "1830", "private", "4361", "1936", "1976", "spiders", "atlantic 10 (a - 10)"], ["sewanee , tennessee", "1857", "private", "1560", "1923", "1932", "tigers", "saa ( ncaa division iii )"], ["columbia , south carolina", "1801", "public", "31288", "1922", "1953", "gamecocks", "sec"], ["knoxville , tennessee", "1794", "public", "27523", "1921", "1932", "volunteers", "sec"], ["new orleans , louisiana", "1834", "private", "13359", "1922", "1932", "green wave", "c - usa (american in 2014)"], ["nashville , tennessee", "1873", "private", "12745", "1922", "1932", "commodores", "sec"], ["charlottesville , virginia", "1819", "public", "21095", "1921", "1937", "cavaliers", "acc"], ["lexington , virginia", "1839", "public", "1500", "1924", "2003", "keydets", "big south (re - joining socon in 2014)"], ["blacksburg , virginia", "1872", "public", "31087", "1921", "1965", "hokies", "acc"], ["winston - salem , north carolina", "1834", "private", "7432", "1936", "1953", "demon deacons", "acc"], ["lexington , virginia", "1749", "private", "2203", "1921", "1958", "generals", "odac ( ncaa division iii )"], ["morgantown , west virginia", "1867", "public", "29707", "1950", "1968", "mountaineers", "big 12"], ["williamsburg , virginia", "1693", "public", "8258", "1936", "1977", "tribe", "caa"]]}, "native_split": "test", "question": "Can you identify which data points deviate significantly from the norm?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No anomalies are detected in the table.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "764095679b6ee3fc079e732afebe4b29"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:766afe58ffd3cac9bbdec711f8b9b2ef", "source": "tablebench", "table_key": "26757f387641a20184f2fae05717f0c339390991", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 4, "section_rows": [], "cells": [["Temperature T (°C)", "Speed of sound c (m/s)", "Density of air ρ (kg/m3)", "Characteristic specific acoustic impedance z0 (Pa·s/m)"], ["35", "351.88", "1.1455", "403.2"], ["30", "349.02", "1.1644", "406.5"], ["25", "346.13", "1.1839", "409.4"], ["20", "343.21", "1.2041", "413.3"], ["15", "340.27", "1.2250", "416.9"], ["10", "337.31", "1.2466", "420.5"], ["5", "334.32", "1.2690", "424.3"], ["0", "331.30", "1.2922", "428.0"], ["−5", "328.25", "1.3163", "432.1"], ["−10", "325.18", "1.3413", "436.1"], ["−15", "322.07", "1.3673", "440.3"], ["−20", "318.94", "1.3943", "444.6"], ["−25", "315.77", "1.4224", "449.1"]]}, "native_split": "test", "question": "Which has a greater causal influence on the speed of sound c (m/s), the density of air ρ (kg/m³) or the temperature T (°C)?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: Yes, Higher interest positively influences deposit balances change (correlation coefficient of 0.89).\",\nFinal Answer: No, Analysis reveals a negligible inverse correlation (0.21), suggesting the gdp does not causally influence the population.\nFinal Answer: The water level of a river exhibits a stronger causal relationship with rainfall (0.82) compared to snowfall (0.32).\n\nEnsure the final answer format is the last output line and the answer should give the conclusion then provide a brief explanation of the causal analysis results as concise as possible.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Temperature T (°C) has a positive influence on the speed of sound c (m/s) (correlation coefficient of 1), while air density ρ (kg/m³) has an equally strong negative influence (correlation coefficient of -1).", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "766afe58ffd3cac9bbdec711f8b9b2ef"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:fb02efe3a1c329c715cb8d0644dcbc02", "source": "tablebench", "table_key": "83f080ace256a317ac2ef1989b9d5a6ca7dc0fc4", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 6, "section_rows": [], "cells": [["draw", "language", "artist", "english translation", "place", "points"], ["1", "finnish", "marion rung", "chirpy chirp", "7", "4"], ["2", "french", "fud leclerc", "your name", "13", "0"], ["3", "spanish", "victor balaguer", "call me", "13", "0"], ["4", "german", "eleonore schwarz", "only in the vienna air", "13", "0"], ["5", "danish", "ellen winther", "lullaby", "10", "2"], ["6", "swedish", "inger berggren", "sun and spring", "7", "4"], ["7", "german", "conny froboess", "two little italians", "6", "9"], ["8", "dutch", "de spelbrekers", "-", "13", "0"], ["9", "french", "isabelle aubret", "a first love", "1", "26"], ["10", "norwegian", "inger jacobsen", "come sun , come rain", "10", "2"], ["11", "french", "jean philippe", "the return", "10", "2"], ["12", "serbian", "lola novaković", "don't turn the lights on at twilight", "4", "10"], ["13", "english", "ronnie carroll", "-", "4", "10"], ["14", "french", "camillo felgen", "little chap", "3", "11"], ["15", "italian", "claudio villa", "goodbye , goodbye", "9", "3"], ["16", "french", "françois deguelt", "say nothing", "2", "13"]]}, "native_split": "test", "question": "What is the median points scored by artists who sang in languages other than French?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "fb02efe3a1c329c715cb8d0644dcbc02"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:db726244de06704566b54e2158b78eaf", "source": "tablebench", "table_key": "e9db5d18ef70a8d4f47f6a382aa87e236dedad2a", "grid": {"TD": 1, "LD": 0, "nrows": 31, "ncols": 5, "section_rows": [], "cells": [["breed", "group", "2002 registrations", "2005 registrations", "2011 registrations"], ["bloodhound", "hound group", "80", "104", "59"], ["bull terrier (miniature)", "terrier group", "278", "275", "216"], ["collie (smooth)", "pastoral group", "85", "72", "75"], ["dandie dinmont terrier", "terrier group", "148", "149", "98"], ["english setter", "gundog group", "568", "1500", "234"], ["english toy terrier (black and tan)", "toy group", "56", "103", "95"], ["fox terrier (smooth)", "terrier group", "167", "212", "137"], ["glen of imaal terrier", "terrier group", "48", "45", "67"], ["gordon setter", "gundog group", "250", "309", "306"], ["greyhound", "hound group", "24", "49", "14"], ["irish red and white setter", "gundog group", "99", "120", "83"], ["irish terrier", "terrier group", "198", "270", "277"], ["kerry blue terrier", "terrier group", "244", "277", "10"], ["king charles spaniel", "toy group", "150", "193", "180"], ["lakeland terrier", "terrier group", "269", "330", "247"], ["lancashire heeler", "pastoral group", "125", "166", "98"], ["manchester terrier", "terrier group", "86", "140", "152"], ["norwich terrier", "terrier group", "153", "131", "158"], ["otterhound", "hound group", "54", "50", "38"], ["retriever (curly coated)", "gundog group", "79", "82", "72"], ["scottish deerhound", "hound group", "231", "264", "237"], ["sealyham terrier", "terrier group", "58", "58", "63"], ["skye terrier", "terrier group", "59", "30", "44"], ["soft coated wheaten terrier", "terrier group", "277", "321", "433"], ["spaniel (clumber)", "gundog group", "170", "192", "271"], ["spaniel (field)", "gundog group", "84", "86", "55"], ["spaniel (irish water)", "gundog group", "145", "106", "117"], ["spaniel (sussex)", "gundog group", "82", "77", "68"], ["welsh corgi (cardigan)", "pastoral group", "56", "77", "108"], ["welsh terrier", "terrier group", "270", "326", "415"]]}, "native_split": "test", "question": "Can you identify which breeds have unusually high or low registrations in specific years?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The two anomalies are the unusually high 2005 registrations for 'english setter' (1500), and the unusually low 2011 registrations for 'kerry blue terrier' (10).", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "db726244de06704566b54e2158b78eaf"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:ce760118539d5455482744e3607aaf15", "source": "tablebench", "table_key": "43c1e1706a4f3f3775a87b7059b93b41683eb34a", "grid": {"TD": 1, "LD": 0, "nrows": 49, "ncols": 7, "section_rows": [], "cells": [["name", "2011 census", "2006 census", "% change", "land area (km square)", "density (pop / km square)", "population rank"], ["algoma district", "115870", "117461", "- 1.4", "48840.68", "2.4", "21"], ["brant county", "136035", "125099", "8.7", "1093.16", "124.4", "17"], ["bruce county", "66102", "65349", "1.2", "4087.76", "16.2", "36"], ["chatham - kent , municipality of", "104075", "108589", "- 4.2", "2470.69", "42.1", "25"], ["cochrane district", "81122", "82503", "- 1.7", "141270.41", "0.6", "33"], ["dufferin county", "56881", "54436", "4.5", "1486.31", "38.3", "41"], ["durham regional municipality", "608124", "561258", "8.4", "2523.62", "241.0", "5"], ["elgin county", "87461", "85351", "2.5", "1880.9", "46.5", "29"], ["essex county", "388782", "393402", "- 1.2", "1850.78", "210.1", "12"], ["frontenac county", "149738", "143865", "4.1", "3787.79", "39.5", "15"], ["greater sudbury , city of", "160376", "157909", "1.6", "3238.01", "49.5", "14"], ["grey county", "92568", "92411", "0.2", "4513.21", "20.5", "28"], ["haldimand - norfolk", "109118", "107812", "1.2", "2894.82", "37.7", "23"], ["haliburton county", "17026", "16147", "5.4", "4071.86", "4.2", "48"], ["halton regional municipality", "501669", "439206", "14.2", "964.01", "520.4", "8"], ["hamilton , city of", "519949", "504559", "3.1", "1117.23", "465.4", "6"], ["hastings county", "134934", "130474", "3.4", "6103.48", "22.1", "18"], ["huron county", "59100", "59325", "- 0.4", "3399.63", "17.4", "38"], ["kawartha lakes , city of", "73214", "74561", "- 1.8", "3083.06", "23.7", "35"], ["kenora district", "57607", "64419", "- 10.6", "407213.01", "0.1", "40"], ["lambton county", "126199", "128204", "- 1.6", "3002.07", "42.0", "20"], ["lanark county", "65867", "63785", "3.0", "3003.82", "21.6", "37"], ["leeds and grenville , united counties of", "99306", "99206", "0.1", "3383.92", "29.3", "27"], ["lennox and addington county", "41824", "40542", "3.2", "2841.1", "14.7", "43"], ["manitoulin district", "13048", "12631", "3.3", "3107.11", "4.2", "49"], ["middlesex county", "439151", "422333", "4.0", "3317.54", "132.4", "10"], ["muskoka district municipality", "58047", "57563", "0.8", "3937.76", "14.7", "39"], ["niagara regional municipality", "431346", "427421", "0.9", "1854.25", "232.6", "11"], ["nipissing district", "84736", "84688", "0.1", "17103.52", "5.0", "31"], ["northumberland county", "82126", "80963", "1.4", "1905.34", "43.1", "32"], ["ottawa , city of", "883391", "812129", "8.8", "2790.22", "316.6", "4"], ["oxford county", "105719", "102756", "2.9", "2039.56", "51.8", "24"], ["parry sound district", "42162", "40918", "3.0", "9322.8", "4.5", "42"], ["peel regional municipality", "1296814", "1159455", "11.8", "1246.89", "1040.0", "2"], ["perth county", "75112", "74344", "1.0", "2218.46", "33.9", "34"], ["peterborough county", "134933", "133080", "1.4", "3847.77", "35.1", "19"], ["prescott and russell , united counties of", "85381", "80184", "6.5", "2004.44", "42.6", "30"], ["prince edward county", "25258", "25496", "- 0.9", "1050.45", "24.0", "45"], ["rainy river district", "20370", "21564", "- 5.5", "15484.83", "1.3", "47"], ["renfrew county", "101326", "97545", "3.9", "7440.81", "13.6", "26"], ["simcoe county", "446063", "422204", "5.7", "4859.16", "91.8", "9"], ["stormont , dundas and glengarry , united counties of", "111164", "110399", "0.7", "3308.84", "33.6", "22"], ["sudbury district", "21196", "21851", "- 3.0", "40205.41", "0.5", "46"], ["thunder bay district", "146057", "149063", "- 2.0", "103719.51", "1.4", "16"], ["timiskaming district", "32634", "33283", "- 1.9", "13299.92", "2.5", "44"], ["toronto , city of", "2615060", "2503281", "4.5", "630.21", "4149.5", "1"], ["waterloo regional municipality", "507096", "478121", "6.1", "1368.94", "370.4", "7"], ["wellington county", "208360", "200425", "4.0", "2660.46", "78.3", "13"]]}, "native_split": "test", "question": "What is the correlation between the 'density (pop / km square)' and '% change' in population across the districts and counties listed in the table? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No correlation, 0.26", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ce760118539d5455482744e3607aaf15"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:9bbb4e79fd68658cd40f7088028db149", "source": "tablebench", "table_key": "06af815904b212f25bfaad83a60f4a51333603af", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 12, "section_rows": [], "cells": [["order", "couple", "karen", "nicky", "jason", "ruthie", "robin", "total", "scoreboard", "song", "public vote %", "result"], ["1", "gareth & maria", "4.5", "4.0", "3.5", "3.5", "4.0", "19 , 5", "3rd", "wake me up when september ends - green day", "6.81%", "safe"], ["2", "linda & daniel", "3.5", "3.0", "3.0", "4.0", "3.0", "16.5", "5th", "candyman - christina aguilera", "9.09%", "safe"], ["3", "samantha & pavel", "3.5", "3.0", "3.0", "3.5", "3.0", "16.0", "7th", "you can't hurry love - the supremes", "3.30%", "eliminated"], ["4", "chris & frankie", "5.0", "5.0", "4.0", "4.5", "5.0", "23.5", "1st", "rule the world - take that", "19.20%", "safe"], ["5", "aggie & sergey", "2.5", "2.0", "2.0", "3.5", "2.5", "12.5", "10th", "total eclipse of the heart - bonnie tyler", "5.00%", "safe"], ["6", "steve & susie", "3.0", "3.5", "2.0", "3.0", "3.0", "14.5", "9th", "mony mony - billy idol", "4.68%", "bottom two"], ["7", "greg & kristina", "3.5", "3.5", "2.5", "3.0", "3.0", "15.5", "8th", "licence to kill - gladys knight", "12.90%", "safe"], ["8", "zaraah & fred", "4.0", "4.5", "3.0", "3.5", "3.5", "18.5", "4th", "take a chance on me - abba", "7.88%", "safe"]]}, "native_split": "test", "question": "Could you describe the structure and content of the table, highlighting the main columns and offering initial insights into the data presented?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The table presents the shooting accuracy of 8 different bullet types (including .308 Winchester and .300 Winchester Magnum) at 100 meters and 300 meters, measured in millimeters and Minutes of Angle (MOA) for dispersion. The data indicates that .300 Winchester Magnum bullets exhibit higher precision at 300 meters, with smaller dispersion ranges.\n\nEnsure the final answer format is the last output line and the answer should give a brief description of the table and provide descriptive explanations for the main columns and offer some basic insights about the table.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "The table details the performance metrics of dance couples in a competition, encompassing scores from five judges, cumulative scores, rankings, song selections, public voting percentages, and competition outcomes. It elucidates the interplay between judges' scores and public votes in determining the final results for each couple.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9bbb4e79fd68658cd40f7088028db149"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:cda1bf9df5bc8991cf90095d46d8c9d2", "source": "tablebench", "table_key": "ff38a284f881839688c29ac79374b58e24c14b89", "grid": {"TD": 1, "LD": 0, "nrows": 25, "ncols": 8, "section_rows": [], "cells": [["rank", "rank fortune 500", "name", "headquarters", "revenue (millions)", "profit (millions)", "employees", "industry"], ["1", "17", "sinopec", "beijing", "131636.0", "3703.1", "681900", "oil"], ["2", "24", "china national petroleum", "beijing", "110520.2", "13265.3", "1086966", "oil"], ["3", "29", "state grid corporation", "beijing", "107185.5", "2237.7", "1504000", "utilities"], ["4", "170", "industrial and commercial bank of china", "beijing", "36832.9", "6179.2", "351448", "banking"], ["5", "180", "china mobile limited", "beijing", "35913.7", "6259.7", "130637", "telecommunications"], ["6", "192", "china life insurance", "beijing", "33711.5", "173.9", "77660", "insurance"], ["7", "215", "bank of china", "beijing", "30750.8", "5372.3", "232632", "banking"], ["8", "230", "china construction bank", "beijing", "28532.3", "5810.3", "297506", "banking"], ["9", "237", "china southern power grid", "guangzhou", "27966.1", "1074.1", "178053", "utilities"], ["10", "275", "china telecom", "beijing", "24791.3", "2279.7", "400299", "telecommunications"], ["11", "277", "agricultural bank of china", "beijing", "24475.5", "728.4", "452464", "banking"], ["12", "290", "hutchison whampoa", "hong kong", "23661.0", "2578.3", "220000", "various sectors"], ["13", "299", "sinochem corporation", "beijing", "23109.2", "344.7", "20343", "various sectors"], ["14", "307", "baosteel", "shanghai", "22663.4", "1622.2", "91308", "steel"], ["15", "342", "china railway engineering", "beijing", "20520.4", "142.6", "275866", "railway"], ["16", "384", "china railway construction", "beijing", "18735.7", "70.2", "245540", "railway"], ["17", "385", "first automotive works", "changchun", "18710.7", "70.0", "136010", "automobile"], ["18", "396", "china state construction", "beijing", "18163.2", "281.3", "294309", "construction"], ["19", "402", "saic motor", "shanghai", "18010.1", "89.7", "72416", "automobile"], ["20", "405", "cofco limited", "beijing", "17953.2", "281.0", "82481", "various sectors"], ["21", "435", "china minmetals", "beijing", "16902.2", "154.4", "32594", "metal trading"], ["22", "457", "jardine matheson", "hong kong / hamilton", "16281.0", "1348.0", "240000", "various sectors"], ["23", "469", "china national offshore oil", "beijing", "16038.9", "3007.1", "44000", "oil"], ["24", "488", "china ocean shipping", "beijing", "15413.5", "1092.9", "79616", "shipping"]]}, "native_split": "test", "question": "Which factors in the table, such as 'industry', 'revenue (millions)', or 'employees', significantly influence the 'profit (millions)' values for the companies listed? If none have an effect, please reply 'no clear impact'.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "revenue (millions)", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "cda1bf9df5bc8991cf90095d46d8c9d2"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:73cb636df01548c38396262253f15f4f", "source": "tablebench", "table_key": "0a19831b5e3e1b23d6b2ae5ffdd86c688876eac6", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 7, "section_rows": [], "cells": [["player", "tackles", "solo", "assisted", "sack", "yards", "td 's"], ["rex motes", "26.0", "24", "4", "0", "0", "0"], ["nathan creer", "25.5", "24", "3", "0", "0", "0"], ["walter holman", "21.0", "17", "8", "2", "3", "0"], ["pete stubbs", "19.5", "18", "3", "3", "0", "0"], ["michael witteck", "16.0", "14", "4", "2", "0", "0"], ["jon roehlk", "15.0", "11", "8", "6", "0", "0"], ["dwayne dixon", "13.0", "12", "2", "0", "0", "0"], ["sean mcinerney", "9.0", "8", "2", "3", "0", "0"], ["robert goins", "9.0", "9", "0", "0", "0", "0"], ["richard dupree", "8.5", "6", "5", "0", "0", "0"], ["brett wilson", "8.0", "7", "2", "0", "0", "0"], ["wes walton", "7.0", "4", "6", "2", "0", "0"], ["fernando mcwherter", "5.0", "5", "0", "0", "0", "0"], ["mike calhoun", "3.0", "2", "2", "3", "0", "0"], ["kendall walls", "2.0", "2", "0", "0", "0", "0"], ["steve griffin", "1.0", "1", "0", "0", "0", "0"]]}, "native_split": "test", "question": "Which is the main factor in the table, such as 'solo', 'assisted', 'sack', 'yards', and 'td 's', significantly contribute to the 'tackles' total for each player?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a entity name or a impact description(No clear impact, Negtive impact or Positive impact), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "solo", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "73cb636df01548c38396262253f15f4f"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:7c54c117f3afcf34f3f904f00ea42c62", "source": "tablebench", "table_key": "86974e13d310c142fad98882be9ad190ac49f022", "grid": {"TD": 1, "LD": 0, "nrows": 31, "ncols": 5, "section_rows": [], "cells": [["Works no.", "Year built", "NGR no.", "SAR no.", "SAR Class"], ["18829", "1909", "330", "1446", "3R"], ["18830", "1909", "331", "1447", "3R"], ["18831", "1909", "332", "1448", "3R"], ["18832", "1909", "333", "1449", "3R"], ["18833", "1909", "334", "1450", "3R"], ["19217", "1910", "345", "1451", "3R"], ["19218", "1910", "346", "1452", "3R"], ["19219", "1910", "347", "1453", "3R"], ["19220", "1910", "348", "1454", "3R"], ["19221", "1910", "349", "1455", "3R"], ["19222", "1910", "350", "1456", "3R"], ["19223", "1910", "351", "1457", "3"], ["19224", "1910", "352", "1458", "3R"], ["19225", "1910", "353", "1459", "3R"], ["19226", "1910", "354", "1460", "3R"], ["19227", "1910", "355", "1461", "3R"], ["19228", "1910", "356", "1462", "3R"], ["19229", "1910", "357", "1463", "3R"], ["19230", "1910", "358", "1464", "3R"], ["19231", "1910", "359", "1465", "3R"], ["19232", "1910", "360", "1466", "3R"], ["19233", "1910", "361", "1467", "3R"], ["19234", "1910", "362", "1468", "3R"], ["19235", "1910", "363", "1469", "3R"], ["19236", "1910", "364", "1470", "3R"], ["19237", "1910", "365", "1471", "3R"], ["19238", "1910", "366", "1472", "3R"], ["19239", "1910", "367", "1473", "3R"], ["19240", "1910", "368", "1474", "3R"], ["19241", "1910", "369", "1475", "3R"]]}, "native_split": "test", "question": "What is the mean and standard deviation of the Year built column?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1909.67, 0.51", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "7c54c117f3afcf34f3f904f00ea42c62"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:db1f2cac2692f10d66aee19c3bc2ae6c", "source": "tablebench", "table_key": "2f1615c68b1f0728613fed268639bfee6ef0a5b5", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["ecozone", "area (km square) territorial waters", "area (km square) exclusive economic zone", "percentage of total area (foreez)", "percentage of marine area (foreez)"], ["pacific marine", "102920", "457646", "3.1", "8.3"], ["arctic basin marine", "24997", "704849", "4.8", "12.7"], ["arctic archipelago marine", "2051393", "2178998", "14.8", "39.3"], ["northwest atlantic marine", "536895", "1205981", "8.2", "21.8"], ["atlantic marine", "72144", "996439", "6.8", "17.9"], ["total", "2788349", "5543913", "37.7", "100.0"]]}, "native_split": "test", "question": "What is the correlation between the 'area (km square)' of territorial waters and the 'percentage of total area (foreez)' for each ecozone? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Strong positive correlation, 0.92", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "db1f2cac2692f10d66aee19c3bc2ae6c"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:c7bc350bc9bde43c892968a9664344be", "source": "tablebench", "table_key": "02cb20b814cf6a34737bfef9bd4a757bfd3ad235", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["physical property", "helium", "neon", "argon", "krypton", "xenon"], ["boiling point (degree)", "268.8", "245.9", "185.8", "151.7", "106.6"], ["melting point (degree)", "-", "248.5", "189.6", "157.4", "111.5"], ["critical temperature (k)", "5.25", "44.5", "150.85", "209.35", "289.74"], ["critical pressure (atm)", "2.26", "26.9", "48.3", "54.3", "57.64"], ["critical density (g / ml)", "0.0693", "0.484", "0.536", "0.908", "1.1"], ["triple point temperature (k)", "24.562", "83.8", "115.76", "161.37", "202.0"], ["triple point pressure (kpa)", "5.1", "43.37", "68.9", "73.15", "81.66"]]}, "native_split": "test", "question": "What is the correlation between the 'boiling point' and 'critical temperature' of noble gases in the table? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Strong negative correlation, -1.0", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c7bc350bc9bde43c892968a9664344be"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:d53d9f75072d62e5f6f39dc0518c15a0", "source": "tablebench", "table_key": "f838f8bc92bf5fbca75d5b7384b8dc3e6b22fcc0", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 6, "section_rows": [], "cells": [["year", "theme", "artist", "finish", "issue price", "total mintage"], ["2002", "golden tulip", "anthony testa", "proof (selectively gold plated)", "24.95", "19986"], ["2003", "golden daffodil", "christie paquet", "proof (selectively gold plated)", "34.95", "36293"], ["2004", "golden easter lily", "christie paquet", "proof (selectively gold plated)", "34.95", "23486"], ["2005", "golden rose", "christie paquet", "proof (selectively gold plated)", "34.95", "23000"], ["2006", "golden daisy", "christie paquet", "proof (selectively gold plated)", "34.95", "23000"], ["2007", "golden forget - me - not", "christie paquet", "proof (selectively gold plated)", "38.95", "20000"]]}, "native_split": "test", "question": "Given the pattern of coin production from 2002 to 2007, including the distribution of themes and issue prices, forecast the likely issue price of coins that would have been produced in 2008.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\n[Answer Example]\nFinal Answer: 1, 2, 3\nFinal Answer: Increasing trend\nFinal Answer: Increasing trend, 13.5\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number, entity name or a trend description(No clear trend, Increasing trend or Decreasing trend), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "40.95", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d53d9f75072d62e5f6f39dc0518c15a0"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:817e2a1847eb77bb39b7e4f60c74cc84", "source": "tablebench", "table_key": "48195d7b12eb3feeeb1ada1e0370ad9eed2a5ef9", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 7, "section_rows": [], "cells": [["season", "series", "races", "poles", "wins", "points", "final placing"], ["2003", "formula renault monza winter series", "2", "0", "0", "18", "8th"], ["2004", "formula renault monza", "16", "3", "5", "375", "1st"], ["2004", "formula junior 1600 spain", "9", "6", "4", "119", "1st"], ["2004", "formula renault 1600 belgium", "4", "0", "1", "65", "11th"], ["2005", "austrian fomula three championship", "7", "6", "3", "75", "1st"], ["2005", "british formula three", "5", "0", "0", "0", "nc"], ["2005", "formula renault 2.0 italia", "0", "0", "0", "0", "nc"], ["2005", "recaro formel 3 cup", "3", "1", "0", "0", "nc"], ["2006", "formula three euroseries", "19", "0", "0", "12", "15th"], ["2006", "british formula three", "2", "0", "0", "0", "nc"], ["2006", "masters of formula three", "1", "0", "0", "n / a", "13th"], ["2007", "formula renault 3.5 series", "14", "0", "0", "0", "nc"], ["2007", "formula three euroseries", "2", "0", "0", "0", "nc"], ["2008", "gp2 asia series", "8", "0", "0", "0", "23rd"], ["2008", "gp2 series", "13", "0", "0", "0", "30th"], ["2008 - 09", "gp2 asia series", "11", "0", "0", "0", "33rd"], ["2009", "gp2 series", "20", "0", "0", "0", "23rd"], ["2009", "formula renault 3.5 series", "6", "0", "0", "7", "23rd"], ["2009 - 10", "gp2 asia series", "8", "0", "0", "7", "13th"], ["2010", "gp2 series", "20", "0", "0", "12", "16th"], ["2011", "gp2 asia series", "4", "0", "0", "9", "8th"], ["2011", "gp2 series", "18", "0", "0", "1", "21st"]]}, "native_split": "test", "question": "What is the correlation between the 'poles' and 'wins' columns across different series and seasons? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Strong positive correlation, 0.86", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "817e2a1847eb77bb39b7e4f60c74cc84"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:3e91d53f7b003e5d9ec55fdb87ee40fb", "source": "tablebench", "table_key": "9e16897e7072ab7c5a7aca77cd11609196f2b9c7", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 5, "section_rows": [], "cells": [["year", "total support and revenue", "total expenses", "increase in net assets", "net assets at end of year"], ["2003 / 2004", "80129", "23463", "56666", "56666"], ["2004 / 2005", "379088", "177670", "211418", "268084"], ["2005 / 2006", "1508039", "791907", "736132", "1004216"], ["2006 / 2007", "2734909", "2077843", "654066", "1658282"], ["2007 / 2008", "5032981", "3540724", "3519886", "5178168"], ["2008 / 2009", "8658006", "5617236", "3053599", "8231767"], ["2009 / 2010", "17979312", "10266793", "6310964", "14542731"], ["2010 / 2011", "24785092", "17889794", "9649413", "24192144"], ["2011 / 2012", "38479665", "29260652", "10736914", "34929058"]]}, "native_split": "test", "question": "What is the projected net asset value at the end of 2012/2013 based on the historical trend?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\n[Answer Example]\nFinal Answer: 1, 2, 3\nFinal Answer: Increasing trend\nFinal Answer: Increasing trend, 13.5\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number, entity name or a trend description(No clear trend, Increasing trend or Decreasing trend), as short as possible, without any explanation. \n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "30416145.92", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3e91d53f7b003e5d9ec55fdb87ee40fb"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:979a0eff0ecb9837c0a9c7968160830e", "source": "tablebench", "table_key": "88d33a9f000ecc238a900bc75b15fbfe6ed851db", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 7, "section_rows": [], "cells": [["no", "name", "mi from kingston", "km from kingston", "parish", "length feet", "length meters"], ["1", "scotts pass", "44.25", "71.2", "clarendon", "70", "21.3"], ["2", "scotts pass", "44.5", "71.6", "clarendon", "170", "51.8"], ["3", "comfort hall", "65.5", "105.4", "st elizabeth", "688", "209.1"], ["4", "balaclava", "70.0", "112.6", "st elizabeth", "348", "106.1"], ["5", "highworth", "84.0", "135.2", "st elizabeth", "182", "55.5"], ["6", "y s", "84.5", "136.0", "st elizabeth", "218", "66.4"], ["7", "ipswich", "86.25", "138.8", "st elizabeth", "855", "260.6"], ["8", "unnamed", "87.75", "141.2", "st james", "555", "164.6"], ["9", "merrywood", "88.5", "142.4", "st james", "362", "115.8"], ["10", "anchovy", "104.5", "168.2", "st james", "102", "31.1"], ["11", "ramble", "108.0", "173.8", "st james", "182", "55.5"], ["12", "bogue hill", "108.5", "174.6", "st james", "1276", "388.9"]]}, "native_split": "test", "question": "What is the correlation between the 'distance from Kingston (km)' and 'road length (m)' in the table? Provide the correlation coefficient as evidence.", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: CorrelationRelation, CorrelationCoefficient.\n\n[Answer Examples]\nFinal Answer: No correlation, 0.22\nFinal Answer: Strong positive correlation, 0.82\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: CorrelationRelation, CorrelationCoefficient.\" form, no other form.\nEnsure that: the correlation coefficient should be a float number with two decimal places; the correlation relation can only be \"No correlation\" with the correlation coefficient between -0.3 to +0.3, \"Weak positive correlation\" with the correlation coefficient between +0.3 to +0.7, \"Weak negative correlation\" with the correlation coefficient between -0.3 to -0.7, \"Strong positive correlation\" with the correlation coefficient between +0.7 to +1, or \"Strong negative correlation\" with the correlation coefficient between -0.7 to -1.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Weak positive correlation, 0.34", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "979a0eff0ecb9837c0a9c7968160830e"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:4d86bad3c182bd35e2958e230f323af5", "source": "tablebench", "table_key": "02cb20b814cf6a34737bfef9bd4a757bfd3ad235", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["physical property", "helium", "neon", "argon", "krypton", "xenon"], ["boiling point (degree)", "268.8", "245.9", "185.8", "151.7", "106.6"], ["melting point (degree)", "-", "248.5", "189.6", "157.4", "111.5"], ["critical temperature (k)", "5.25", "44.5", "150.85", "209.35", "289.74"], ["critical pressure (atm)", "2.26", "26.9", "48.3", "54.3", "57.64"], ["critical density (g / ml)", "0.0693", "0.484", "0.536", "0.908", "1.1"], ["triple point temperature (k)", "24.562", "83.8", "115.76", "161.37", "202.0"], ["triple point pressure (kpa)", "5.1", "43.37", "68.9", "73.15", "81.66"]]}, "native_split": "test", "question": "Can you calculate the mean, median, and standard deviation of the boiling points for the five noble gases, and determine which gas has the most extreme boiling point relative to the mean?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "191.76, 185.8, 66.63, xenon", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "4d86bad3c182bd35e2958e230f323af5"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:bdb2b51671800b9bb71404b1017c807f", "source": "tablebench", "table_key": "78d9bc96347873da4dc5b80a27758b085be84dcd", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["country", "area km square", "population", "population density per km square", "hdi (2011)", "capital"], ["china (prc)", "9640011", "1339724852", "138", "0.699", "beijing"], ["hong kong (prc)", "1104", "7061200", "6390", "0.898", "hong kong"], ["japan", "377930", "127950000", "337", "0.901", "tokyo"], ["macau (prc)", "30", "556800", "18662", "no data", "macau"], ["mongolia", "1564100", "2809600", "2", "0.653", "ulaanbaatar"], ["north korea", "120538", "24346000", "198", "no data", "pyongyang"], ["south korea", "100210", "48988833", "500", "0.897", "seoul"]]}, "native_split": "test", "question": "Which country has unusually data points compared to the other countries in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below as in examples:\n[Answer Format]\nFinal Answer: Answer.\n\n[Answer Examples]\nFinal Answer: The three anomalies are row 5 with Tom having an unusually high score 101 in the Math column, row 7 with an unusually low score 3 in the English column, and row 9 with an unusually high score 200 in the Science column.\nFinal Answer: No anomalies are detected in the table.\n\nEnsure the final answer format is the last output line and the answer should point out the abnormal data with total number then explain why for each anomaly as short as possible. If no anomaly is detected, the answer should be \"No anomalies are detected in the table.\"\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "No anomalies are detected in the table.", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "bdb2b51671800b9bb71404b1017c807f"}, "dataset": "tablebench", "subtype": "DataAnalysis"}
{"id": "tablebench:dp-test:0024501faeb895ca098242a0af2f822a", "source": "tablebench", "table_key": "47c6a8717d9962434695686e604e144bac3fce98", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 6, "section_rows": [], "cells": [["rank", "nation", "gold", "silver", "bronze", "total"], ["1", "italy", "5", "4", "2", "11"], ["2", "france", "3", "3", "5", "11"], ["3", "egypt", "2", "0", "2", "4"], ["4", "algeria", "1", "4", "4", "9"], ["5", "tunisia", "1", "1", "5", "7"], ["6", "turkey", "1", "1", "0", "2"], ["7", "slovenia", "1", "0", "1", "2"], ["8", "serbia and montenegro", "0", "1", "2", "3"], ["9", "spain", "0", "0", "5", "5"], ["10 =", "greece", "0", "0", "1", "1"], ["10 =", "morocco", "0", "0", "1", "1"]]}, "native_split": "test", "question": "According to the table, which nation won 1 gold and 4 silver medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "algeria", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0024501faeb895ca098242a0af2f822a"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:b19bad70a2dd3e356e8c6d038fa2bfd3", "source": "tablebench", "table_key": "584bd76214306d715f8092b6e1fe3c426d63626d", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 6, "section_rows": [], "cells": [["Year", "Award", "Category", "Nominated Work", "Result", "Notes"], ["2017", "Global Music Awards", "Jazz Music Album", "Bein' Green", "Silver Medal", "-"], ["2017", "Global Music Awards", "Album", "Bein' Green", "Silver Medal", "-"], ["2017", "Hollywood Music in Media Awards", "Jazz", "Sunny Morning", "Nominated", "edition #8 (2017)"], ["2017", "Radio Music Awards", "Jazz", "Sunny Morning", "Won", "-"], ["2017", "Radio Music Awards", "Jazz", "Flying Away", "Won", "-"], ["2018", "16th Independent Music Awards", "Jazz", "Funky Party", "Won", "Vox Populi Award"], ["2018", "Indie Music Channel Awards", "Jazz Song", "Flying Away", "Won", "-"], ["2018", "Indie Music Channel Awards", "Jazz Recording", "Tears For Niro", "Won", "-"], ["2018", "Indie Music Channel Awards", "Jazz Instrumentalist", "Funky Party", "Won", "-"], ["2018", "Indie Music Channel Awards", "Jazz Producer", "Cabriolet", "Won", "-"], ["2018", "Indie Music Channel Awards", "Jazz Video", "Sunny Morning", "Won", "Natalia Vlaskina co-winner"], ["2018", "Indie Music Channel Awards", "Best New Male Artist of the Year", "-", "Won", "-"], ["2018", "Indie Music Channel Awards", "Recording of the Year", "Tears For Niro", "-", "-"], ["2018", "Hollywood Music in Media Awards", "Jazz", "Funky Party", "Nominated", "edition #9 (2018)"], ["2018", "Annual TheMothFM Jazz Awards (GMFM -DB Radio Group)", "Best Overall Artist 2018", "-", "Winner", "-"], ["2018", "UK Songwriting Contest 2018", "Jazz/Blues", "Flying Away", "Finalist", "-"], ["2018", "UK Songwriting Contest 2018", "Jazz/Blues", "Funky Party", "Finalist", "-"], ["2018", "UK Songwriting Contest 2018", "Jazz/Blues", "Tears For Niro", "Finalist", "-"], ["2018", "UK Songwriting Contest 2018", "Jazz/Blues", "With You All The Clouds Go Away", "Finalist", "-"], ["2019", "17th Independent Music Awards 2019", "Instrumental", "Lullaby of Christmas", "nominee", "Winner TBA in June 2019"]]}, "native_split": "test", "question": "What is the award that \"Sunny Morning\" won in 2017, and in which edition of the Hollywood Music in Media Awards was it nominated?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Radio Music Awards, edition #8 (2017)", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b19bad70a2dd3e356e8c6d038fa2bfd3"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:fb233753896ca878c04484eeb4f019b9", "source": "tablebench", "table_key": "90ef4829cff4656a2d5c0e7299de3befe5a7e8f4", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 11, "section_rows": [], "cells": [["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["oakdale rfc", "22", "2", "0", "614", "226", "88", "23", "13", "0", "97"], ["blaenavon rfc", "22", "1", "5", "444", "271", "61", "33", "5", "2", "73"], ["brynithel rfc", "22", "3", "4", "398", "292", "41", "24", "4", "1", "71"], ["caldicot rfc", "22", "0", "8", "500", "330", "69", "44", "8", "3", "67"], ["usk rfc", "22", "2", "8", "484", "431", "71", "58", "11", "1", "64"], ["hartridge rfc", "22", "1", "11", "424", "345", "52", "45", "5", "5", "52"], ["bettws rfc", "22", "3", "11", "476", "438", "59", "53", "6", "7", "51"], ["rtb (ebbw vale) rfc", "22", "3", "12", "317", "371", "38", "50", "5", "4", "43"], ["ynysddu rfc", "22", "1", "14", "315", "376", "35", "44", "3", "9", "42"], ["llanhilleth rfc", "22", "3", "13", "357", "475", "42", "61", "3", "4", "37"], ["trinant rfc", "22", "1", "15", "261", "487", "29", "65", "1", "4", "31"], ["pontllanfraith rfc", "22", "0", "21", "160", "708", "17", "102", "2", "1", "7"]]}, "native_split": "test", "question": "How many points did the llanhilleth rfc score in the league season?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "37", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "fb233753896ca878c04484eeb4f019b9"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:31d0f57a11ed01b643d02e560eee8c05", "source": "tablebench", "table_key": "fc0624f550d7819e8ed4628c553a63dae6662f52", "grid": {"TD": 1, "LD": 0, "nrows": 47, "ncols": 6, "section_rows": [], "cells": [["year", "mens singles", "womens singles", "mens doubles", "womens doubles", "mixed doubles"], ["1964", "feliks glapka , poznań", "teresa masłowska , warszawa", "feliks glapka marian grys , poznań", "no competition", "bolesław suterski stanisława suterska , poznań"], ["1965", "aleksander koczur , kraków", "teresa masłowska , warszawa", "andrzej domagała krzysztof englander , wrocław", "no competition", "bolesław suterski stanisława suterska , poznań"], ["1966", "wiesław świątczak , łódź", "teresa masłowska , warszawa", "andrzej domagała krzysztof englander , wrocław", "no competition", "wiesław świątczak irena józefowicz , łódź"], ["1967", "wiesław świątczak , łódź", "barbara rojewska , olsztyn", "andrzej domagała krzysztof englander , wrocław", "no competition", "krzysztof englander bożena basińska , wrocław"], ["1968", "krzysztof englander , wrocław", "irena karolczak , wrocław", "jerzy przybylski lech woźny , poznań", "no competition", "krzysztof englander irena karolczak , wrocław"], ["1969", "andrzej domagała , wrocław", "teresa masłowska , warszawa", "andrzej domagała krzysztof englander , wrocław", "no competition", "bogusław żołądkowski teresa masłowska , warszawa"], ["1970", "wiesław świątczak , łódź", "irena karolczak , wrocław", "jerzy przybylski lech woźny , poznań", "no competition", "jan makarus jolanta proch , szczecin"], ["1971", "wiesław świątczak , łódź", "lidia baczyńska , wrocław", "andrzej domagała krzysztof englander , wrocław", "no competition", "wiesław świątczak ewa astasiewicz , łódź"], ["1972", "wiesław danielski", "irena karolczak", "wiesław danielski zygmunt skrzypczyński", "lidia baczyńska irena karolczak", "leszek nowakowski hana snochowska"], ["1973", "andrzej domagała", "irena karolczak", "wiesław danielski zygmunt skrzypczyński", "no competition", "sławomir wloszczynski irena karolczak"], ["1974", "stanisław rosko", "irena karolczak", "ryszard borek stanisław rosko", "irena karolczak hana snochowska", "leszek nowakowski hana snochowska"], ["1975", "zygmunt skrzypczyński", "irena karolczak", "andrzej domagała wiesław świątczak", "irena karolczak hana snochowska", "leslaw markowicz irena karolczak"], ["1976", "zygmunt skrzypczyński", "elżbieta utecht", "krzysztof englander janusz labisko", "irena karolczak wanda czamańska", "leslaw markowicz irena karolczak"], ["1978", "zygmunt skrzypczyński", "elżbieta utecht", "zygmunt skrzypczyński sławomir włoszyński", "bożena wojtkowska elżbieta utecht", "janusz labisko anna zyśk"], ["1979", "brunon rduch", "elżbieta utecht", "zygmunt skrzypczyński sławomir włoszyński", "bożena wojtkowska maria bahryj", "zygmunt skrzypczyński elżbieta utecht"], ["1980", "zygmunt skrzypczyński", "bożena wojtkowska", "zygmunt skrzypczyński janusz labisko", "bożena wojtkowska ewa rusznica", "zygmunt skrzypczyński elżbieta utecht"], ["1981", "brunon rduch", "bożena wojtkowska", "brunon rduch norbert węgrzyn", "bożena wojtkowska zofia żółtańska", "jerzy dołhan ewa rusznica"], ["1982", "stanisław rosko", "bożena wojtkowska", "stanisław rosko kazimierz ciurys", "bożena wojtkowska ewa rusznica", "jerzy dołhan bożena wojtkowska"], ["1983", "stanisław rosko", "ewa rusznica", "jerzy dołhan grzegorz olchowik", "bożena wojtkowska bożena siemieniec", "kazimierz ciurys bożena wojtkowska"], ["1984", "stanisław rosko", "bożena wojtkowska", "jerzy dołhan grzegorz olchowik", "bożena wojtkowska ewa wilman", "kazimierz ciurys bożena wojtkowska"], ["1985", "grzegorz olchowik", "bożena wojtkowska", "jerzy dołhan grzegorz olchowik", "bożena siemieniec zofia żółtańska", "jerzy dołhan ewa wilman"], ["1986", "grzegorz olchowik", "bożena siemieniec", "jerzy dołhan grzegorz olchowik", "bożena siemieniec zofia żółtańska", "jerzy dołhan ewa wilman"], ["1987", "jerzy dołhan", "bożena haracz", "jerzy dołhan grzegorz olchowik", "bożena haracz bożena siemieniec", "jerzy dołhan bożena haracz"], ["1988", "jerzy dołhan", "bożena siemieniec", "jerzy dołhan grzegorz olchowik", "bożena haracz bożena siemieniec", "jerzy dołhan bożena haracz"], ["1989", "jacek hankiewicz", "bożena siemieniec", "jerzy dołhan jacek hankiewicz", "bożena haracz bożena siemieniec", "jerzy dołhan bożena haracz"], ["1990", "jacek hankiewicz", "beata syta", "jerzy dołhan jacek hankiewicz", "bożena haracz beata syta", "jerzy dołhan bożena haracz"], ["1991", "jacek hankiewicz", "katarzyna krasowska", "jerzy dołhan jacek hankiewicz", "bożena haracz bożena siemieniec", "jerzy dołhan bożena haracz"], ["1992", "dariusz zięba", "katarzyna krasowska", "jerzy dołhan jacek hankiewicz", "bożena haracz bożena bąk", "jerzy dołhan bożena haracz"], ["1993", "jacek hankiewicz", "katarzyna krasowska", "dariusz zięba jacek hankiewicz", "bożena haracz bożena bąk", "jerzy dołhan bożena haracz"], ["1994", "dariusz zięba", "katarzyna krasowska", "jerzy dołhan damian pławecki", "monika lipińska sylwia rutkiewicz", "damian pławecki dorota borek"], ["1995", "dariusz zięba", "katarzyna krasowska", "jerzy dołhan damian pławecki", "dorota borek katarzyna krasowska", "jerzy dołhan bożena haracz"], ["1996", "dariusz zięba", "katarzyna krasowska", "dariusz zięba jacek hankiewicz", "monika bienkowska katarzyna boczek", "robert mateusiak sylwia rutkiewicz"], ["1997", "jacek niedźwiedzki", "katarzyna krasowska", "jerzy dołhan damian pławecki", "dorota borek katarzyna krasowska", "damian pławecki dorota borek"], ["1998", "jacek niedźwiedzki", "katarzyna krasowska", "michał łogosz damian pławecki", "bożena haracz katarzyna krasowska", "damian pławecki dorota grzejdak"], ["1999", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "bożena haracz joanna szleszyńska", "robert mateusiak monika bienkowska"], ["2000", "jacek niedźwiedzki", "katarzyna krasowska", "michał łogosz robert mateusiak", "bożena haracz katarzyna krasowska", "robert mateusiak barbara kulanty"], ["2001", "jacek niedźwiedzki", "kamila augustyn", "michał łogosz robert mateusiak", "barbara kulanty joanna szleszyńska", "robert mateusiak barbara kulanty"], ["2002", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn joanna szleszyńska", "robert mateusiak barbara kulanty"], ["2003", "jacek niedźwiedzki", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn paulina matusewicz", "robert mateusiak barbara kulanty"], ["2004", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak barbara kulanty"], ["2005", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak barbara kulanty"], ["2006", "przemysław wacha", "angelika węgrzyn", "rafał hawel przemysław wacha", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak nadieżda kostiuczyk"], ["2007", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak nadieżda kostiuczyk"], ["2008", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak nadieżda kostiuczyk"], ["2009", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "małgorzata kurdelska agnieszka wojtkowska", "robert mateusiak kamila augustyn"], ["2010", "przemysław wacha", "kamila augustyn", "michał łogosz robert mateusiak", "kamila augustyn nadieżda kostiuczyk", "robert mateusiak nadieżda kostiuczyk"]]}, "native_split": "test", "question": "Which women's singles champion has won the most titles among those who have also won a mixed doubles title?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "kamila augustyn", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "31d0f57a11ed01b643d02e560eee8c05"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:d462f4647ce89a206cfe3e7ecba1b28c", "source": "tablebench", "table_key": "2721f971fdbdf44c9a961aad2c9019783744d536", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Brazil", "Representing Brazil", "Representing Brazil", "Representing Brazil", "Representing Brazil", "Representing Brazil"], ["2007", "South American Junior Championships", "São Paulo, Brazil", "1st", "Long jump", "5.79 m"], ["2007", "World Youth Championships", "Ostrava, Czech Republic", "21st", "Heptathlon", "4575 pts"], ["2008", "World Junior Championships", "Bydgoszcz, Poland", "16th", "Heptathlon", "5233 pts"], ["2008", "South American U23 Championships", "Lima, Peru", "1st", "Heptathlon", "5138 pts"], ["2009", "South American Junior Championships", "São Paulo, Brazil", "4th", "100 m", "11.95 s"], ["2009", "South American Junior Championships", "São Paulo, Brazil", "1st", "4 × 100 m", "45.86 s"], ["2009", "South American Junior Championships", "São Paulo, Brazil", "1st", "Heptathlon", "5574 pts"], ["2009", "Pan American Junior Championships", "Port of Spain, Trinidad and Tobago", "1st", "Heptathlon", "5574 pts"], ["2009", "South American Championships", "Lima, Peru", "1st", "Heptathlon", "5578 pts"], ["2010", "South American Games / South American U23 Championships", "Medellín, Colombia", "–", "Heptathlon", "DNF"], ["2010", "Ibero-American Championships", "San Fernando, Spain", "4th", "Heptathlon", "5304 pts"], ["2011", "South American Championships", "Buenos Aires, Argentina", "1st", "Heptathlon", "5428 pts"], ["2012", "South American U23 Championships", "São Paulo, Brazil", "1st", "Heptathlon", "5899 pts"], ["2014", "Ibero-American Championships", "São Paulo, Brazil", "1st", "Heptathlon", "5722 pts"], ["2015", "Pan American Games", "Toronto, Canada", "3rd", "Heptathlon", "6035 pts"], ["2015", "World Championships", "Beijing, China", "26th", "Heptathlon", "5647 pts"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "23rd", "Heptathlon", "6024 pts"], ["2017", "World Championships", "London, United Kingdom", "29th", "Heptathlon", "4500 pts"], ["2017", "Universiade", "Taipei, Taiwan", "5th", "Heptathlon", "5337 pts"]]}, "native_split": "test", "question": "In which year did the athlete win a gold medal in the Heptathlon event at the South American Championships in Peru, and what was the corresponding points score?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2009, 5578 pts", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d462f4647ce89a206cfe3e7ecba1b28c"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:df326b71a2a4177210d685c8bbd6cbf2", "source": "tablebench", "table_key": "7263b2d43d7ef9b205c8cd66a2449f154a14a225", "grid": {"TD": 1, "LD": 0, "nrows": 44, "ncols": 6, "section_rows": [], "cells": [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1.", "United States", "8", "7", "1", "16"], ["2.", "Russia", "7", "7", "5", "19"], ["3.", "France", "3", "3", "2", "8"], ["4.", "Ethiopia", "3", "2", "2", "7"], ["5.", "Belarus", "3", "1", "3", "7"], ["6.", "Sweden", "2", "1", "2", "5"], ["7.", "Kenya", "2", "1", "1", "4"], ["7=", "South Africa", "2", "1", "1", "4"], ["9.", "Morocco", "2", "1", "0", "3"], ["10.", "Greece", "1", "1", "2", "4"], ["11.", "Cuba", "1", "1", "0", "2"], ["12.", "Italy", "1", "0", "2", "3"], ["13.", "Canada", "1", "0", "1", "2"], ["14.", "Algeria", "1", "0", "0", "1"], ["14=", "Australia", "1", "0", "0", "1"], ["14=", "Dominican Republic", "1", "0", "0", "1"], ["14=", "Ecuador", "1", "0", "0", "1"], ["14=", "Lithuania", "1", "0", "0", "1"], ["14=", "Mexico", "1", "0", "0", "1"], ["14=", "Mozambique", "1", "0", "0", "1"], ["14=", "Poland", "1", "0", "0", "1"], ["14=", "Qatar", "1", "0", "0", "1"], ["14=", "Saint Kitts and Nevis", "1", "0", "0", "1"], ["24.", "Jamaica", "0", "4", "2", "6"], ["25.", "Spain", "0", "3", "2", "5"], ["26.", "Hungary", "0", "2", "0", "2"], ["27.", "Germany", "0", "1", "3", "4"], ["27=", "Japan", "0", "1", "3", "4"], ["27=", "Ukraine", "0", "1", "3", "4"], ["30.", "Great Britain", "0", "1", "2", "3"], ["31.", "Brazil", "0", "1", "0", "1"], ["31=", "Cameroon", "0", "1", "0", "1"], ["31=", "Czech Republic", "0", "1", "0", "1"], ["31=", "Estonia", "0", "1", "0", "1"], ["31=", "Ireland", "0", "1", "0", "1"], ["31=", "Trinidad and Tobago", "0", "1", "0", "1"], ["31=", "Turkey", "0", "1", "0", "1"], ["38.", "Bahamas", "0", "0", "3", "3"], ["39.", "China", "0", "0", "2", "2"], ["40.", "India", "0", "0", "1", "1"], ["40=", "Kazakhstan", "0", "0", "1", "1"], ["40=", "Netherlands", "0", "0", "1", "1"], ["40=", "Senegal", "0", "0", "1", "1"]]}, "native_split": "test", "question": "Which nation ranked with a total of 8 medals, including 3 gold medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "France", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "df326b71a2a4177210d685c8bbd6cbf2"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:9949f443a53a970598e8d3e4d400361b", "source": "tablebench", "table_key": "71df161e2d280787b1d070fd364f868ba4255989", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing India", "Representing India", "Representing India", "Representing India", "Representing India", "Representing India"], ["2008", "Asian Junior Championships", "Jakarta, Indonesia", "2nd", "800 m", "2:08.63"], ["2008", "World Junior Championships", "Bydgoszcz, Poland", "8th (sf)", "800m", "2:06.51"], ["2008", "World Junior Championships", "Bydgoszcz, Poland", "14th (h)", "4 × 400 m relay", "3:44.13"], ["2009", "Asian Championships", "Guangzhou, China", "6th", "800 m", "2:07.36"], ["2010", "Commonwealth Games", "Delhi, India", "6th", "800 m", "2:01.25"], ["2010", "Asian Games", "Guangzhou, China", "3rd", "800 m", "2:01.36"], ["2011", "Asian Championships", "Kobe, Japan", "3rd", "800 m", "2:02.55"], ["2011", "Asian Championships", "Kobe, Japan", "2nd", "4 × 400 m relay", "3:44.17"], ["2011", "World Championships", "Daegu, South Korea", "15th (sf)", "800 m", "2:00.95"], ["2012", "Olympic Games", "London, United Kingdom", "10th (sf)", "800 m", "1:59.61"], ["2013", "Asian Championships", "Pune, India", "3rd", "800 m", "2:04.48"], ["2013", "Asian Championships", "Pune, India", "1st", "4 × 400 m relay", "3:32.26"], ["2013", "World Championships", "Moscow, Russia", "15th (h)", "4 × 400 m relay", "3:38.81"], ["2014", "Commonwealth Games", "Glasgow, United Kingdom", "11th (sf)", "800 m", "2:03.35"], ["2014", "Asian Games", "Incheon, South Korea", "2nd", "800 m", "1:59.19"], ["2014", "Asian Games", "Incheon, South Korea", "1st", "4 × 400 m relay", "3:28.68 GR"], ["2015", "Asian Championships", "Wuhan, China", "1st", "800 m", "2:01.53"], ["2015", "Asian Championships", "Wuhan, China", "2nd", "4 × 400 m relay", "3:33.81"], ["2015", "World Championships", "Beijing, China", "19th (h)", "800 metres", "2:00.95"], ["2015", "World Championships", "Beijing, China", "14th (h)", "4 × 400 m relay", "3:29.08"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "29th (h)", "800 m", "2:00.58"]]}, "native_split": "test", "question": "What is the event in which the athlete achieved a position of 2nd in the 2008 Asian Junior Championships?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "800 m", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9949f443a53a970598e8d3e4d400361b"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:be18c465eb0851ea697b232017a9c7dc", "source": "tablebench", "table_key": "f135ce660ab84056be2d55b7c67cdc2219d0a028", "grid": {"TD": 1, "LD": 0, "nrows": 33, "ncols": 6, "section_rows": [], "cells": [["Song", "Year", "Artist", "Album", "Role", "Notes"], ["\"Leavin' Eyes\"", "1984", "Glen Campbell", "Letter to Home", "Writer, producer", "-"], ["\"Cruisin'\"", "1986", "Alabama", "The Touch", "Writer", "-"], ["\"No More Tears\"", "1994", "David Ball", "David Ball", "Writer", "-"], ["\"Love Lessons\"", "1995", "Tracy Byrd", "Love Lessons", "Writer", "US Country #9"], ["\"Wine into Water\"", "1998", "T. Graham Brown", "Wine into Water", "Writer", "US Country #44"], ["\"Don't Think I Won't\"", "1998", "Mark Wills", "Wish You Were Here", "Writer", "-"], ["\"She Rides Wild Horses\"", "1999", "Kenny Rogers", "She Rides Wild Horses", "Writer", "-"], ["\"He Rocks\"", "2000", "Wynonna Judd", "New Day Dawning", "Writer", "-"], ["\"Monkey in the Middle\"", "2003", "Rodney Atkins", "Honesty", "Writer, producer", "-"], ["\"Honesty (Write Me a List)\"", "2003", "Rodney Atkins", "Honesty", "Producer, vocals", "US Country #4"], ["\"Someone to Share it With\"", "2003", "Rodney Atkins", "Honesty", "Writer, producer", "-"], ["\"The Man I Am Today\"", "2003", "Rodney Atkins", "Honesty", "Writer, producer", "-"], ["\"My Old Man\"", "2003", "Rodney Atkins", "Honesty", "Writer, producer", "US Country #36"], ["\"Wasted Whiskey\"", "2006", "Rodney Atkins", "If You're Going Through Hell", "Writer, producer", "-"], ["\"Cleaning This Gun (Come On In Boy)\"", "2006", "Rodney Atkins", "If You're Going Through Hell", "Producer, vocals", "US Country #1 US Gold"], ["\"Watching You\"", "2006", "Rodney Atkins", "If You're Going Through Hell", "Producer, vocals", "US Country #1 US Platinum"], ["\"If You're Going Through Hell (Before the Devil Even Knows)\"", "2006", "Rodney Atkins", "If You're Going Through Hell", "Producer, vocals", "US Country #1 US Platinum"], ["\"These Are My People\"", "2006", "Rodney Atkins", "If You're Going Through Hell", "Producer, vocals", "US Country #1 US Gold"], ["\"Home Sweet Oklahoma\"", "2008", "Patti Page and Vince Gill", "Best Country Songs", "Writer, producer", "-"], ["\"Chasin' Girls\"", "2009", "Rodney Atkins", "It's America", "Writer, producer", "-"], ["\"It's America\"", "2009", "Rodney Atkins", "It's America", "Producer, vocals", "US Country #1"], ["\"15 Minutes\"", "2009", "Rodney Atkins", "It's America", "Producer, vocals", "US Country #20"], ["\"Farmer's Daughter\"", "2010", "Rodney Atkins", "It's America", "Producer, vocals", "US Country #5 US Platinum"], ["\"Growing Up Like That\"", "2011", "Rodney Atkins", "Take a Back Road", "Writer, producer", "-"], ["\"Take a Back Road\"", "2011", "Rodney Atkins", "Take a Back Road", "Producer", "US Country #1 US Platinum"], ["\"He's Mine\"", "2011", "Rodney Atkins", "Take a Back Road", "Producer, vocals", "US Country #23"], ["\"Tips\"", "2011", "Rodney Atkins", "Take a Back Road", "Writer, producer", "-"], ["\"Lifelines\"", "2011", "Rodney Atkins", "Take a Back Road", "Writer, producer", "-"], ["\"Cool with That\"", "2015", "Brett Kissel", "Pick Me Up", "Writer", "-"], ["\"Wine Into Water\"", "2015", "T. Graham Brown", "Forever Changed", "Writer", "-"], ["\"Wine Into Water\"", "2016", "Loretta Lynn", "Full Circle", "Writer", "-"], ["\"Nights in the Sun\"", "2017", "Brett Kissel", "We Were That Song", "Writer", "-"]]}, "native_split": "test", "question": "How many songs written by Glen Campbell were released in the 1980s?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "be18c465eb0851ea697b232017a9c7dc"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:0fb4f4180841f757ff71e13664a49e94", "source": "tablebench", "table_key": "c1837189e4429a49808d06536e4b64e6794eb4c0", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Australia", "Representing Australia", "Representing Australia", "Representing Australia", "Representing Australia", "Representing Australia"], ["2010", "Oceania Youth Championships", "Sydney, Australia", "3rd", "100 m", "12.15 s (+0.5 m/s)"], ["2010", "Oceania Youth Championships", "Sydney, Australia", "1st", "100 m hurdles", "14.12 s (−1.4 m/s)"], ["2010", "Oceania Youth Championships", "Sydney, Australia", "1st", "4 × 100 m relay", "45.75 s"], ["2010", "Youth Olympic Games", "Singapore", "2nd", "100 m hurdles", "13.46"], ["2012", "World Junior Championships", "Barcelona, Spain", "5th", "100 m hurdles", "13.54"], ["2014", "Commonwealth Games", "Glasgow, United Kingdom", "5th", "100 m hurdles", "13.36"], ["2015", "Universiade", "Gwangju, South Korea", "3rd", "100 m hurdles", "12.94"], ["2015", "World Championships", "Beijing, China", "18th (sf)", "100 m hurdles", "13.01"], ["2016", "World Indoor Championships", "Portland, United States", "10th (h)", "60 m hurdles", "8.10"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "37th (h)", "100 m hurdles", "13.26"], ["2017", "World Championships", "London, United Kingdom", "21st (sf)", "100 m hurdles", "13.25"], ["2017", "Universiade", "Taipei, Taiwan", "8th", "100 m hurdles", "14.82"], ["2018", "World Indoor Championships", "Birmingham, United Kingdom", "22nd (sf)", "60 m hurdles", "8.22"], ["2018", "Commonwealth Games", "Gold Coast, Australia", "4th", "100 m hurdles", "13.07"]]}, "native_split": "test", "question": "In which year did the athlete win their first gold medal in the 100 m hurdles event?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2010", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0fb4f4180841f757ff71e13664a49e94"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:c77daa5488bbd256403af096bfea88b2", "source": "tablebench", "table_key": "7e2713fca662562ee32077133f4761c3ebcc9d5e", "grid": {"TD": 1, "LD": 0, "nrows": 47, "ncols": 11, "section_rows": [], "cells": [["ward", "bello", "ben - tahir", "doucet", "furtenbacher", "gauthier", "haydon", "larter", "lawrance", "libweshya", "liscumb"], ["orlãans", "51", "27", "1918", "14", "132", "939", "18", "27", "6", "6"], ["innes", "41", "11", "1466", "11", "105", "638", "10", "7", "7", "5"], ["barrhaven", "36", "32", "1267", "6", "26", "1305", "10", "15", "4", "3"], ["kanata north", "23", "23", "1222", "14", "14", "704", "12", "9", "3", "2"], ["west carleton - march", "6", "5", "958", "2", "10", "909", "3", "8", "2", "1"], ["stittsville", "9", "7", "771", "1", "9", "664", "2", "8", "2", "1"], ["bay", "37", "68", "2009", "20", "38", "1226", "20", "21", "8", "8"], ["college", "40", "32", "2112", "13", "22", "1632", "7", "15", "6", "10"], ["knoxdale - merivale", "33", "47", "1583", "17", "17", "1281", "11", "12", "4", "3"], ["gloucester - southgate", "84", "62", "1378", "25", "39", "726", "15", "20", "12", "8"], ["beacon hill - cyrville", "70", "24", "1297", "7", "143", "592", "7", "10", "1", "6"], ["rideau - vanier", "66", "24", "2148", "15", "261", "423", "11", "14", "11", "4"], ["rideau - rockcliffe", "68", "48", "1975", "15", "179", "481", "11", "19", "8", "6"], ["somerset", "47", "33", "2455", "17", "45", "326", "15", "18", "12", "1"], ["kitchissippi", "39", "21", "3556", "12", "21", "603", "10", "10", "3", "6"], ["river", "52", "57", "1917", "16", "31", "798", "11", "13", "6", "4"], ["capital", "40", "20", "4430", "18", "34", "369", "8", "7", "7", "5"], ["alta vista", "58", "89", "2114", "12", "74", "801", "8", "15", "5", "2"], ["cumberland", "39", "32", "1282", "12", "135", "634", "8", "8", "5", "5"], ["osgoode", "15", "2", "769", "8", "22", "768", "5", "11", "1", "4"], ["rideau - goulbourn", "7", "4", "898", "11", "15", "1010", "1", "7", "1", "4"], ["gloucester - south nepean", "36", "35", "976", "9", "23", "721", "10", "6", "5", "5"], ["kanata south", "29", "26", "1646", "24", "18", "1354", "6", "20", "3", "5"], ["ward", "lyrette", "maguire", "o'brien", "pita", "ryan", "st arnaud", "scharf", "taylor", "watson", "wright"], ["orlãans", "14", "332", "3937", "8", "27", "17", "84", "52", "8685", "14"], ["innes", "5", "229", "2952", "9", "26", "11", "44", "35", "6746", "11"], ["barrhaven", "3", "394", "3335", "14", "20", "4", "46", "46", "5943", "19"], ["kanata north", "3", "209", "2612", "10", "8", "3", "35", "44", "4516", "15"], ["west carleton - march", "1", "297", "3072", "2", "13", "3", "28", "28", "2746", "88"], ["stittsville", "2", "265", "2884", "10", "7", "6", "33", "15", "3195", "8"], ["bay", "9", "299", "3221", "8", "16", "9", "82", "96", "7220", "19"], ["college", "4", "378", "4249", "14", "28", "8", "68", "83", "7668", "21"], ["knoxdale - merivale", "8", "301", "3269", "14", "20", "1", "43", "47", "5540", "18"], ["gloucester - southgate", "7", "288", "3006", "16", "24", "17", "46", "39", "6107", "13"], ["beacon hill - cyrville", "9", "239", "2329", "20", "11", "15", "59", "39", "5484", "7"], ["rideau - vanier", "17", "129", "1503", "10", "11", "17", "58", "58", "5784", "21"], ["rideau - rockcliffe", "18", "139", "1729", "16", "13", "17", "55", "42", "5850", "27"], ["somerset", "8", "126", "1393", "12", "16", "12", "59", "80", "5164", "21"], ["kitchissippi", "6", "211", "2389", "13", "10", "9", "56", "80", "7034", "22"], ["river", "9", "312", "2875", "20", "13", "8", "53", "69", "6539", "27"], ["capital", "5", "140", "1436", "12", "6", "10", "35", "52", "6543", "14"], ["alta vista", "9", "265", "2672", "13", "15", "8", "52", "60", "6666", "22"], ["cumberland", "11", "296", "3203", "6", "25", "7", "53", "40", "6371", "12"], ["osgoode", "6", "441", "3039", "6", "9", "1", "48", "27", "2844", "11"], ["rideau - goulbourn", "2", "649", "3556", "6", "10", "3", "36", "19", "3359", "8"], ["gloucester - south nepean", "8", "247", "2372", "12", "13", "4", "33", "36", "4759", "11"]]}, "native_split": "test", "question": "Which ward has a value of 66 in the \"bello\" column?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "rideau - vanier", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c77daa5488bbd256403af096bfea88b2"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:6f65e885bfa8dc52b0cd93679c9acca7", "source": "tablebench", "table_key": "5ef1982e8bb10fe684c51ecf8126b57539bbc968", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 6, "section_rows": [], "cells": [["year", "winner (number of titles)", "runners - up", "top team in regular season (points)", "top scorer (points)", "winning coach"], ["1968", "atlanta chiefs (1)", "san diego toros", "san diego toros (186 points)", "janusz kowalik", "phil woosnam"], ["1969", "kansas city spurs (1)", "atlanta chiefs", "kansas city spurs (110 points)", "kaizer motaung", "janos bedl"], ["1970", "rochester lancers (1)", "washington darts", "washington darts (137 points)", "kirk apostolidis", "sal derosa"], ["1971", "dallas tornado (1)", "atlanta chiefs", "rochester lancers (141 points)", "carlos metidieri", "ron newman"], ["1972", "new york cosmos (1)", "st louis stars", "new york cosmos (77 points)", "randy horton", "gordon bradley"], ["1973", "philadelphia atoms (1)", "dallas tornado", "dallas tornado (111 points)", "kyle rote , jr", "al miller"], ["1974", "los angeles aztecs (1)", "miami toros", "los angeles aztecs (110 points)", "paul child", "alex perolli"], ["1975", "tampa bay rowdies (1)", "portland timbers", "portland timbers (138 points)", "steve david", "eddie firmani"], ["1976", "toronto metros - croatia (1)", "minnesota kicks", "tampa bay rowdies (154 points)", "giorgio chinaglia", "domagoj kapetanović"], ["1977", "new york cosmos (2)", "seattle sounders", "fort lauderdale strikers (161 points)", "steve david", "eddie firmani"], ["1978", "new york cosmos (3)", "tampa bay rowdies", "new york cosmos (212 points)", "giorgio chinaglia", "eddie firmani"], ["1979", "vancouver whitecaps (1)", "tampa bay rowdies", "new york cosmos (216 points)", "oscar fabbiani", "tony waiters"], ["1980", "new york cosmos (4)", "fort lauderdale strikers", "new york cosmos (213 points)", "giorgio chinaglia", "hennes weisweiler & yasin özdenak"], ["1981", "chicago sting (1)", "new york cosmos", "new york cosmos (200 points)", "giorgio chinaglia", "willy roy"], ["1982", "new york cosmos (5)", "seattle sounders", "new york cosmos (203 points)", "giorgio chinaglia", "julio mazzei"], ["1983", "tulsa roughnecks (1)", "toronto blizzard", "new york cosmos (194 points)", "roberto cabañas", "terry hennessey"]]}, "native_split": "test", "question": "Which team won the most titles in the period between 1968 and 1983, and what was the name of their winning coach in the year they won their first title?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "new york cosmos, gordon bradley", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "6f65e885bfa8dc52b0cd93679c9acca7"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:0c5e7d0d8e33e31f3e80aab314ef8c61", "source": "tablebench", "table_key": "6c4b3b63705ebb0f9b69b6d676a9898925ef3c29", "grid": {"TD": 1, "LD": 0, "nrows": 58, "ncols": 6, "section_rows": [], "cells": [["Outcome", "No.", "Year", "Championship", "Opponent in the final", "Score"], ["Winner", "1.0", "1987", "Grand Prix", "Dennis Taylor", "10–7"], ["Winner", "2.0", "1988", "British Open", "Mike Hallett", "13–2"], ["Runner-up", "1.0", "1988", "UK Championship", "Doug Mountjoy", "12–16"], ["Winner", "3.0", "1989", "Asian Open", "James Wattana", "9–2"], ["Winner", "4.0", "1989", "Dubai Classic", "Doug Mountjoy", "9–2"], ["Winner", "5.0", "1989", "UK Championship", "Steve Davis", "16–12"], ["Runner-up", "2.0", "1989", "International Open", "Steve Davis", "4–9"], ["Runner-up", "3.0", "1990", "European Open", "John Parrott", "6–10"], ["Winner", "6.0", "1990", "World Snooker Championship", "Jimmy White", "18–12"], ["Winner", "7.0", "1990", "Grand Prix (2)", "Nigel Bond", "10–5"], ["Winner", "8.0", "1990", "Asian Open (2)", "Dennis Taylor", "9–3"], ["Winner", "9.0", "1990", "Dubai Classic (2)", "Steve Davis", "9–1"], ["Winner", "10.0", "1990", "UK Championship (2)", "Steve Davis", "16–15"], ["Runner-up", "4.0", "1991", "Classic", "Jimmy White", "4–10"], ["Winner", "11.0", "1991", "British Open (2)", "Gary Wilkinson", "10–9"], ["Winner", "12.0", "1991", "Grand Prix (3)", "Steve Davis", "10–6"], ["Winner", "13.0", "1992", "Welsh Open", "Darren Morgan", "9–3"], ["Runner-up", "5.0", "1992", "Classic (2)", "Steve Davis", "8–9"], ["Winner", "14.0", "1992", "World Snooker Championship (2)", "Jimmy White", "18–14"], ["Runner-up", "6.0", "1992", "Dubai Classic", "John Parrott", "8–9"], ["Runner-up", "7.0", "1993", "European Open (2)", "Steve Davis", "4–10"], ["Winner", "15.0", "1993", "International Open", "Steve Davis", "10–6"], ["Winner", "16.0", "1993", "World Snooker Championship (3)", "Jimmy White", "18–5"], ["Winner", "17.0", "1993", "Dubai Classic (3)", "Steve Davis", "9–3"], ["Runner-up", "8.0", "1993", "UK Championship (2)", "Ronnie O'Sullivan", "6–10"], ["Winner", "18.0", "1993", "European Open", "Ronnie O'Sullivan", "9–5"], ["Winner", "19.0", "1994", "World Snooker Championship (4)", "Jimmy White", "18–17"], ["Winner", "20.0", "1994", "UK Championship (3)", "Ken Doherty", "10–5"], ["Winner", "21.0", "1994", "European Open (2)", "John Parrott", "9–3"], ["Winner", "22.0", "1995", "World Snooker Championship (5)", "Nigel Bond", "18–9"], ["Winner", "23.0", "1995", "Grand Prix (4)", "John Higgins", "9–5"], ["Winner", "24.0", "1995", "UK Championship (4)", "Peter Ebdon", "10–3"], ["Winner", "25.0", "1996", "World Snooker Championship (6)", "Peter Ebdon", "18–12"], ["Winner", "26.0", "1996", "UK Championship (5)", "John Higgins", "10–9"], ["Winner", "27.0", "1997", "Welsh Open (2)", "Mark King", "9–2"], ["Winner", "28.0", "1997", "International Open (2)", "Tony Drago", "9–1"], ["Runner-up", "9.0", "1997", "British Open", "Mark Williams", "2–9"], ["Runner-up", "10.0", "1997", "World Snooker Championship", "Ken Doherty", "12–18"], ["Runner-up", "11.0", "1997", "UK Championship (3)", "Ronnie O'Sullivan", "6–10"], ["Winner", "29.0", "1998", "Thailand Masters (3)", "John Parrott", "9–6"], ["Runner-up", "12.0", "1998", "British Open (2)", "John Higgins", "8–9"], ["Runner-up", "13.0", "1999", "Welsh Open", "Mark Williams", "8–9"], ["Winner", "30.0", "1999", "Scottish Open (3)", "Graeme Dott", "9–1"], ["Winner", "31.0", "1999", "World Snooker Championship (7)", "Mark Williams", "18–11"], ["Winner", "32.0", "1999", "British Open (3)", "Peter Ebdon", "9–1"], ["Runner-up", "14.0", "2000", "Thailand Masters", "Mark Williams", "5–9"], ["Runner-up", "15.0", "2001", "Thailand Masters (2)", "Ken Doherty", "3–9"], ["Winner", "33.0", "2001", "European Open (3)", "Joe Perry", "9–2"], ["Runner-up", "16.0", "2002", "World Snooker Championship (2)", "Peter Ebdon", "17–18"], ["Winner", "34.0", "2003", "Welsh Open (3)", "Mark Williams", "9–5"], ["Runner-up", "17.0", "2003", "European Open (3)", "Ronnie O'Sullivan", "6–9"], ["Winner", "35.0", "2003", "British Open (4)", "Ronnie O'Sullivan", "9–6"], ["Runner-up", "18.0", "2003", "UK Championship (4)", "Matthew Stevens", "8–10"], ["Runner-up", "19.0", "2005", "Welsh Open (2)", "Ronnie O'Sullivan", "8–9"], ["Winner", "36.0", "2005", "Malta Cup (4)", "Graeme Dott", "9–7"], ["Runner-up", "20.0", "2005", "China Open", "Ding Junhui", "5–9"], ["Runner-up", "21.0", "2006", "UK Championship (5)", "Peter Ebdon", "6–10"]]}, "native_split": "test", "question": "Which player has won the most championships in the 1990s?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Steve Davis", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0c5e7d0d8e33e31f3e80aab314ef8c61"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:04ee0f17194c6801d956b34508e32420", "source": "tablebench", "table_key": "fe9d0ddabd75a1e05e0657fa8f05aaed5eb305d6", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 7, "section_rows": [], "cells": [["year", "venue", "location", "winning team", "score", "us captain", "international captain"], ["2013", "muirfield village", "dublin , ohio", "united states", "18½ - 15½", "fred couples", "nick price"], ["2011", "royal melbourne golf club", "melbourne , australia", "united states", "19 - 15", "fred couples", "greg norman"], ["2009", "harding park golf club", "san francisco , california", "united states", "19½ - 14½", "fred couples", "greg norman"], ["2007", "royal montreal golf club", "montreal , canada", "united states", "19½ - 14½", "jack nicklaus", "gary player"], ["2005", "robert trent jones golf club", "gainesville , virginia", "united states", "18½ - 15½", "jack nicklaus", "gary player"], ["2003", "fancourt hotel and country club", "george , western cape , south africa", "tied", "17 - 17", "jack nicklaus", "gary player"], ["2000", "robert trent jones golf club", "gainesville , virginia", "united states", "21½ - 10½", "ken venturi", "peter thomson"], ["1998", "royal melbourne golf club", "melbourne , australia", "international", "20½ - 11½", "jack nicklaus", "peter thomson"], ["1996", "robert trent jones golf club", "gainesville , virginia", "united states", "16½ - 15½", "arnold palmer", "peter thomson"], ["1994", "robert trent jones golf club", "gainesville , virginia", "united states", "20 - 12", "hale irwin", "david graham"]]}, "native_split": "test", "question": "How many times did the captain who led the United States to the most victories captain the team?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "04ee0f17194c6801d956b34508e32420"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:d2db16edd343270475522cf1f70b86c6", "source": "tablebench", "table_key": "630984ffd106ceddb681e7b7ad0da30c4b25b060", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 7, "section_rows": [], "cells": [["shirt number", "player name", "games played", "total points", "2 - points", "3 - points", "free throw"], ["11", "alpha ibrahim koroma", "5", "31", "30", "0", "1"], ["12", "alpha jalloh", "5", "17", "16", "0", "1"], ["13", "samuel juah", "3", "7", "6", "0", "1"], ["14", "abdulai bangura", "2", "4", "4", "0", "0"], ["15", "ibrahim jalloh", "5", "6", "6", "0", "0"], ["17", "ibrahim kallon", "4", "6", "2", "3", "1"], ["18", "ibrahim bakarr kamara", "3", "2", "2", "0", "0"], ["22", "alpha ambrose kargbo", "5", "60", "42", "15", "3"], ["33", "amadu kargbo", "4", "14", "12", "0", "2"]]}, "native_split": "test", "question": "How many 3-pointers did Alpha Ambrose Kargbo make in the given games?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "15", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d2db16edd343270475522cf1f70b86c6"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:78ccc4fb07ce60975392c8b42aa454ea", "source": "tablebench", "table_key": "6f9da72057f4362f10faba24177502f6c4645f9a", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["Player", "Rec.", "Yards", "Avg.", "TD's", "Long"], ["Charles Frederick", "115", "1385", "12", "27", "42"], ["Jerel Myers", "104", "1183", "11.4", "21", "38"], ["Anthony Hines", "58", "822", "14.2", "12", "39"], ["Boo Williams", "45", "474", "10.5", "17", "35"], ["Ira Gooch", "24", "339", "14.1", "6", "32"], ["Sam Simmons", "15", "197", "13.1", "2", "30"], ["Kevin Beard", "10", "87", "8.7", "0", "21"], ["Dawan Moss", "7", "39", "5.6", "1", "12"], ["Cyron Brown", "3", "17", "5.7", "1", "8"], ["Larrell Johnson", "3", "14", "4.7", "0", "6"], ["Jamarr Wood", "1", "13", "13", "0", "13"], ["Cecil Moore", "2", "9", "4.5", "2", "8"], ["Raymond Philyaw", "1", "−6", "−6", "0", "−6"]]}, "native_split": "test", "question": "Which player had an average of 11.4 yards per reception?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Jerel Myers", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "78ccc4fb07ce60975392c8b42aa454ea"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:f10d21dbe9cca173c388760beaa75c80", "source": "tablebench", "table_key": "d08de22d98c2f5ba9c5dfd99310bb55a8d8ee7d2", "grid": {"TD": 1, "LD": 0, "nrows": 41, "ncols": 7, "section_rows": [], "cells": [["name", "league goals", "fa cup goals", "fl cup goals", "other goals", "total", "career"], ["george brown", "142", "17", "0", "0", "159", "1921 - 1929"], ["jimmy glazzard", "142", "12", "0", "0", "154", "1946 - 1956"], ["andy booth", "133", "5", "4", "8", "150", "1991 - 1996 and 2001 - 2009"], ["billy smith", "114", "12", "0", "0", "126", "1913 - 1934"], ["les massie", "100", "6", "2", "0", "108", "1956 - 1966"], ["vic metcalfe", "87", "3", "0", "0", "90", "1946 - 1958"], ["alex jackson", "70", "19", "0", "0", "89", "1925 - 1930"], ["jordan rhodes", "73", "2", "6", "6", "87", "2009 - 2012"], ["frank mann", "68", "7", "0", "0", "75", "1912 - 1923"], ["dave mangnall", "61", "12", "0", "0", "73", "1929 - 1934"], ["derek stokes", "65", "2", "2", "0", "69", "1960 - 1965"], ["kevin mchale", "60", "5", "3", "0", "68", "1956 - 1967"], ["iwan roberts", "50", "4", "6", "8", "68", "1990 - 1993"], ["ian robins", "59", "5", "3", "0", "67", "1978 - 1982"], ["marcus stewart", "58", "2", "7", "0", "67", "1996 - 2000"], ["mark lillis", "56", "4", "3", "0", "63", "1978 - 1985"], ["charlie wilson", "57", "5", "0", "0", "62", "1922 - 1925"], ["alan gowling", "58", "1", "2", "0", "61", "1972 - 1975"], ["craig maskell", "43", "3", "4", "4", "55", "1988 - 1990"], ["brian stanton", "45", "6", "3", "0", "54", "1979 - 1986"], ["colin dobson", "50", "0", "2", "0", "52", "1966 - 1970"], ["ernie islip", "44", "8", "0", "0", "52", "1913 - 1923"], ["paweł abbott", "48", "1", "2", "0", "51", "2004 - 2007"], ["clem stephenson", "42", "8", "0", "0", "50", "1921 - 1929"], ["david cowling", "43", "2", "3", "0", "48", "1978 - 1987"], ["duncan shearer", "38", "3", "6", "1", "48", "1986 - 1988"], ["frank worthington", "41", "5", "2", "0", "48", "1967 - 1972"], ["charlie luke", "40", "7", "0", "0", "47", "1931 - 1936"], ["phil starbuck", "36", "4", "2", "5", "47", "1991 - 1995"], ["jimmy lawson", "42", "4", "0", "0", "46", "1968 - 1976"], ["alf lythgoe", "42", "4", "0", "0", "46", "1934 - 1938"], ["george mclean", "43", "3", "0", "0", "46", "1930 - 1934"], ["danny schofield", "39", "1", "0", "6", "46", "1998 - 2008"], ["peter fletcher", "36", "4", "5", "0", "45", "1978 - 1982"], ["sammy taylor", "39", "6", "0", "0", "45", "1919 - 1921"], ["tony leighton", "40", "2", "2", "0", "44", "1965 - 1968"], ["ronnie jepson", "36", "3", "2", "1", "42", "1993 - 1996"], ["bob kelly", "39", "3", "0", "0", "42", "1927 - 1932"], ["lee novak", "34", "5", "2", "1", "42", "2009 - 2013"], ["terry gray", "36", "2", "3", "0", "41", "1973 - 1979"]]}, "native_split": "test", "question": "According to the table, how many league goals did george mclean score during his career from 1930 - 1934?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "43", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f10d21dbe9cca173c388760beaa75c80"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:7b4144d4fe9ab8f84ad391281875782a", "source": "tablebench", "table_key": "4999c5993514032bc7d421ec4895b6d931de369b", "grid": {"TD": 1, "LD": 0, "nrows": 36, "ncols": 6, "section_rows": [], "cells": [["year", "senior pga championship", "the tradition", "senior players championship", "us senior open", "senior british open"], ["2013", "kōki idoki", "david frost", "kenny perry (1 / 2)", "kenny perry (2 / 2)", "mark wiebe"], ["2012", "roger chapman (1 / 2)", "tom lehman (3 / 3)", "joe daley", "roger chapman (2 / 2)", "fred couples (2 / 2)"], ["2011", "tom watson (6 / 6)", "tom lehman (2 / 3)", "fred couples (1 / 2)", "olin browne", "russ cochran"], ["2010", "tom lehman (1 / 3)", "fred funk (3 / 3)", "mark o'meara", "bernhard langer (2 / 2)", "bernhard langer (1 / 2)"], ["2009", "michael allen", "mike reid (2 / 2)", "jay haas (3 / 3)", "fred funk (2 / 3)", "loren roberts (4 / 4)"], ["2008", "jay haas (2 / 3)", "fred funk (1 / 3)", "d a weibring", "eduardo romero (2 / 2)", "bruce vaughan"], ["2007", "denis watson", "mark mcnulty", "loren roberts (3 / 4)", "brad bryant", "tom watson (5 / 6)"], ["2006", "jay haas (1 / 3)", "eduardo romero (1 / 2)", "bobby wadkins", "allen doyle (4 / 4)", "loren roberts (2 / 4)"], ["2005", "mike reid (1 / 2)", "loren roberts (1 / 4)", "peter jacobsen (2 / 2)", "allen doyle (3 / 4)", "tom watson (4 / 6)"], ["2004", "hale irwin (7 / 7)", "craig stadler (2 / 2)", "mark james", "peter jacobsen (1 / 2)", "pete oakley"], ["2003", "john jacobs", "tom watson (3 / 6)", "craig stadler (1 / 2)", "bruce lietzke", "tom watson (2 / 6)"], ["2002", "fuzzy zoeller", "jim thorpe", "stewart ginn", "don pooley", "not a champions tour event"], ["2001", "tom watson (1 / 6)", "doug tewell (2 / 2)", "allen doyle (2 / 4)", "bruce fleisher", "not a champions tour event"], ["2000", "doug tewell (1 / 2)", "tom kite", "raymond floyd (4 / 4)", "hale irwin (6 / 7)", "not a champions tour event"], ["1999", "allen doyle (1 / 4)", "graham marsh (2 / 2)", "hale irwin (5 / 7)", "dave eichelberger", "not a champions tour event"], ["1998", "hale irwin (3 / 7)", "gil morgan (2 / 3)", "gil morgan (3 / 3)", "hale irwin (4 / 7)", "not a champions tour event"], ["1997", "hale irwin (2 / 7)", "gil morgan (1 / 3)", "larry gilbert", "graham marsh (1 / 2)", "not a champions tour event"], ["1996", "hale irwin (1 / 7)", "jack nicklaus (8 / 8)", "raymond floyd (3 / 4)", "dave stockton (3 / 3)", "not a champions tour event"], ["1995", "raymond floyd (2 / 4)", "jack nicklaus (7 / 8)", "j c snead", "tom weiskopf", "not a champions tour event"], ["1994", "lee trevino (4 / 4)", "raymond floyd (1 / 4)", "dave stockton (2 / 3)", "simon hobday", "not a champions tour event"], ["1993", "tom wargo", "tom shaw", "jack nicklaus (6 / 8)", "jim colbert", "not a champions tour event"], ["1992", "lee trevino (2 / 4)", "lee trevino (3 / 4)", "dave stockton (1 / 3)", "larry laoretti", "not a champions tour event"], ["1991", "jack nicklaus (3 / 8)", "jack nicklaus (5 / 8)", "jim albus", "jack nicklaus (4 / 8)", "not a champions tour event"], ["1990", "gary player (6 / 6)", "jack nicklaus (1 / 8)", "jack nicklaus (2 / 8)", "lee trevino (1 / 4)", "not a champions tour event"], ["1989", "larry mowry", "don bies", "orville moody (2 / 2)", "orville moody (1 / 2)", "not a champions tour event"], ["1988", "gary player (4 / 6)", "founded in 1989", "billy casper (2 / 2)", "gary player (5 / 6)", "not a champions tour event"], ["1987", "chi chi rodriguez (2 / 2)", "founded in 1989", "gary player (3 / 6)", "gary player (2 / 6)", "not a champions tour event"], ["1986", "gary player (1 / 6)", "founded in 1989", "chi chi rodriguez (1 / 2)", "dale douglass", "founded in 1987"], ["1985", "not held", "founded in 1989", "arnold palmer (5 / 5)", "miller barber (5 / 5)", "founded in 1987"], ["1984", "dec peter thomson", "founded in 1989", "arnold palmer (4 / 5)", "miller barber (4 / 5)", "founded in 1987"], ["1984", "jan arnold palmer (3 / 5)", "founded in 1989", "arnold palmer (4 / 5)", "miller barber (4 / 5)", "founded in 1987"], ["1983", "not held", "founded in 1989", "miller barber (3 / 5)", "billy casper (1 / 2)", "founded in 1987"], ["1982", "don january", "founded in 1989", "founded in 1983", "miller barber (2 / 5)", "founded in 1987"], ["1981", "miller barber (1 / 5)", "founded in 1989", "founded in 1983", "arnold palmer (2 / 5)", "founded in 1987"], ["1980", "arnold palmer (1 / 5)", "founded in 1989", "founded in 1983", "roberto devicenzo", "founded in 1987"]]}, "native_split": "test", "question": "In which year did this athlete win both the Senior PGA Championship and Senior Players Championship titles？", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1986", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "7b4144d4fe9ab8f84ad391281875782a"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:993a7fd34ef053762ab118cd5ae0a3c0", "source": "tablebench", "table_key": "c591d8e010c2d3143852a165a88faf50a56f2ae8", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["year", "indians admitted", "pakistanis admitted", "sri lankans admitted", "bangladeshis admitted", "nepalis admitted"], ["2000", "26122", "14201", "5849", "2715", "247"], ["2001", "27901", "15353", "5520", "3393", "273"], ["2002", "28838", "14173", "4968", "2615", "418"], ["2003", "24595", "12351", "4448", "1896", "440"], ["2004", "25573", "12793", "4134", "3374", "594"], ["2005", "22141", "13575", "4690", "3940", "714"], ["2006", "30746", "12329", "4490", "3838", "640"], ["2007", "26047", "9545", "3934", "2735", "564"], ["2008", "24548", "8051", "4508", "2716", "639"], ["2009", "26117", "6213", "4270", "4270", "627"], ["2010", "30252", "4986", "4181", "4364", "1502"], ["2011", "24965", "6073", "3104", "2449", "1249"], ["2012", "28943", "9931", "3152", "2449", "1311"], ["total", "346788", "139574", "57248", "35856", "9218"]]}, "native_split": "test", "question": "How many Indians were admitted in 2005?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "22141", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "993a7fd34ef053762ab118cd5ae0a3c0"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:28bf1ccc00e7ac7016bde04933ece3e4", "source": "tablebench", "table_key": "cbc91d8cb691e0ef5aee9ace6da64b7e87acf4dc", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 6, "section_rows": [], "cells": [["Year", "Association", "Category", "Work", "Result", "Ref."], ["1938", "Academy Awards", "Best Actress in a Leading Role", "Stella Dallas", "Nominated", "-"], ["1942", "Academy Awards", "Best Actress in a Leading Role", "Ball of Fire", "Nominated", "-"], ["1945", "Academy Awards", "Best Actress in a Leading Role", "Double Indemnity", "Nominated", "-"], ["1949", "Academy Awards", "Best Actress in a Leading Role", "Sorry, Wrong Number", "Nominated", "-"], ["1960", "Hollywood Walk of Fame", "Motion Pictures, 1751 Vine Street", "-", "Won", "-"], ["1961", "Emmy Awards", "Outstanding Performance by an Actress in a Series", "The Barbara Stanwyck Show", "Won", "-"], ["1966", "Emmy Awards", "Outstanding Continued Performance by an Actress in a Leading Role", "The Big Valley", "Won", "-"], ["1966", "Golden Globe Awards", "Best TV Star – Female", "The Big Valley", "Nominated", "-"], ["1967", "Emmy Awards", "Outstanding Continued Performance by an Actress in a Leading Role", "The Big Valley", "Nominated", "-"], ["1967", "Golden Globe Awards", "Best TV Star – Female", "The Big Valley", "Nominated", "-"], ["1967", "Screen Actors Guild", "Life Achievement", "-", "Won", "-"], ["1968", "Emmy Awards", "Outstanding Continued Performance by an Actress in a Leading Role", "The Big Valley", "Nominated", "-"], ["1968", "Golden Globe Awards", "Best TV Star – Female", "The Big Valley", "Nominated", "-"], ["1973", "Hall of Great Western Performers Cowboy Hall of Fame Oklahoma City", "Lifetime Achievement Award Performer", "-", "Won", "-"], ["1981", "Film Society of Lincoln Center Gala Tribute", "-", "-", "Won", "-"], ["1981", "Los Angeles Film Critics Association", "Career Achievement", "-", "Won", "-"], ["1982", "Academy Awards", "Honorary Award", "-", "Won", "-"], ["1983", "Emmy Awards", "Outstanding Lead Actress in a Limited Series", "The Thorn Birds", "Won", "-"], ["1984", "Golden Globe Awards", "Best Performance by an Actress in a Supporting Role", "The Thorn Birds", "Won", "-"], ["1986", "Golden Globe Awards", "Cecil B. DeMille Award", "-", "Won", "-"], ["1987", "American Film Institute", "Life Achievement", "-", "Won", "-"]]}, "native_split": "test", "question": "In which year did Barbara Stanwyck win an Emmy Award for a TV series?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1961", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "28bf1ccc00e7ac7016bde04933ece3e4"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:2d9cd413bf7d15b7e92be8a8a9a12efb", "source": "tablebench", "table_key": "83b1e0eae410bdfe6d98f62fa29ece3686d40bf9", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["Year", "Award", "Category", "Work", "Result", "Ref."], ["2002", "Online Film & Television Association Awards", "Best Youth Performance", "Road to Perdition", "Nominated", "-"], ["2002", "Saturn Awards", "Best Performance by a Younger Actor", "Road to Perdition", "Won", "-"], ["2003", "Broadcast Film Critics Association Awards", "Best Young Actor/Actress", "Road to Perdition", "Nominated", "-"], ["2003", "Las Vegas Film Critics Society Awards", "Youth in Film", "Road to Perdition", "Nominated", "-"], ["2003", "Phoenix Film Critics Society Awards", "Best Performance by a Youth in a Leading or Supporting Role – Male", "Road to Perdition", "Nominated", "-"], ["2003", "Young Artist Award", "Best Performance in a Feature Film – Leading Young Actor", "Road to Perdition", "Won", "-"], ["2004", "Teen Choice Awards", "Choice Breakout TV Star – Male", "7th Heaven", "Nominated", "-"], ["2005", "Teen Choice Awards", "Choice TV Actor: Drama", "7th Heaven", "Nominated", "-"], ["2005", "Young Artist Award", "Best Performance in a TV Series (Comedy or Drama) – Leading Young Actor", "7th Heaven", "Nominated", "-"], ["2008", "Fright Meter Awards", "Best Supporting Actor", "Solstice", "Nominated", "-"], ["2013", "Young Hollywood Awards", "Best Ensemble (shared with Holland Roden, Crystal Reed, Dylan O'Brien and Tyler Posey)", "Teen Wolf", "Won", "-"], ["2014", "Teen Choice Awards", "Choice TV: Male Scene Stealer", "Teen Wolf", "Won", "-"], ["2017", "Saturn Awards", "Best Guest Performance on a Television Series", "Supergirl", "Nominated", "-"]]}, "native_split": "test", "question": "Which award did the individual win in 2003 for their performance in the movie \"Road to Perdition\"?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Young Artist Award", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "2d9cd413bf7d15b7e92be8a8a9a12efb"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:01029e3c959b99916ea9d73a107a87bf", "source": "tablebench", "table_key": "543cf718b19f2ac99fc484a601d92b0e5eb1e86c", "grid": {"TD": 1, "LD": 0, "nrows": 32, "ncols": 8, "section_rows": [], "cells": [["seed", "rank", "player", "points", "points defending", "points won", "new points", "status"], ["1", "1", "rafael nadal", "12070", "2000", "1200", "11270", "runner - up , lost to novak djokovic"], ["2", "2", "novak djokovic", "12005", "720", "2000", "13285", "champion , defeated rafael nadal"], ["3", "3", "roger federer", "9230", "360", "360", "9230", "quarterfinals lost to jo - wilfried tsonga"], ["4", "4", "andy murray", "6855", "720", "720", "6855", "semifinals lost to rafael nadal"], ["5", "5", "robin söderling", "4595", "360", "90", "4325", "third round lost to bernard tomic (q)"], ["6", "7", "tomáš berdych", "3490", "1200", "180", "2470", "fourth round lost to mardy fish"], ["7", "6", "david ferrer", "4150", "180", "180", "4150", "fourth round lost to jo - wilfried tsonga"], ["8", "10", "andy roddick", "2200", "180", "90", "2110", "third round lost to feliciano lópez"], ["9", "8", "gaël monfils", "2780", "90", "90", "2780", "third round lost to łukasz kubot (q)"], ["10", "9", "mardy fish", "2335", "45", "360", "2650", "quarterfinals lost rafael nadal"], ["11", "11", "jürgen melzer", "2175", "180", "90", "2085", "third round lost to xavier malisse"], ["12", "19", "jo - wilfried tsonga", "1585", "360", "720", "1945", "semifinals lost to novak djokovic"], ["13", "12", "viktor troicki", "1930", "45", "45", "1930", "second round lost to lu yen - hsun"], ["14", "14", "stanislas wawrinka", "1900", "10", "45", "1935", "second round lost to simone bolelli (ll)"], ["15", "16", "gilles simon", "1745", "90", "90", "1745", "third round lost to juan martín del potro"], ["16", "15", "nicolás almagro", "1875", "10", "90", "1955", "third round lost to mikhail youzhny"], ["17", "13", "richard gasquet", "1925", "0", "180", "2105", "fourth round lost to andy murray"], ["18", "17", "mikhail youzhny", "1740", "45", "180", "1875", "fourth round lost to roger federer"], ["19", "35", "michaël llodra", "1195", "45", "180", "1330", "fourth round lost vs novak djokovic"], ["20", "18", "florian mayer", "1600", "90", "45", "1555", "second round lost to xavier malisse"], ["21", "23", "fernando verdasco", "1425", "10", "45", "1460", "second round lost to robin haase"], ["22", "21", "alexandr dolgopolov", "1405", "45", "10", "1370", "first round lost to fernando gonzález (pr)"], ["23", "29", "janko tipsarević", "1305", "10", "10", "1305", "first round lost to ivo karlović"], ["24", "22", "juan martín del potro", "1445", "0", "180", "1625", "fourth round lost to rafael nadal"], ["25", "20", "juan ignacio chela", "1475", "10", "45", "1505", "second round lost to alex bogomolov , jr"], ["26", "31", "guillermo garcía - lópez", "1120", "10", "45", "1155", "second round lost to karol beck (q)"], ["27", "26", "marin čilić", "1345", "10", "10", "1345", "first round lost to ivan ljubičić"], ["28", "24", "david nalbandian", "1425", "0", "90", "1515", "third round lost to roger federer"], ["29", "27", "nikolay davydenko", "1330", "45", "10", "1295", "first round lost to bernard tomic (q)"], ["30", "28", "thomaz bellucci", "1305", "90", "10", "1225", "first round lost to rainer schüttler"], ["31", "25", "milos raonic", "1354", "0", "45", "1399", "second round lost to gilles müller (wc)"]]}, "native_split": "test", "question": "According to the table, which player won the championship and how many points did they win?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "novak djokovic, 2000", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "01029e3c959b99916ea9d73a107a87bf"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:9c67f976517e940f4c621cc43685f9f1", "source": "tablebench", "table_key": "d12ed0051456afda3a969573a0386299bef68f6a", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing China", "Representing China", "Representing China", "Representing China", "Representing China", "Representing China"], ["2013", "Asian Championships", "Pune, India", "4th", "Triple jump", "13.57 m"], ["2014", "Asian Indoor Championships", "Hangzhou, China", "3rd", "Triple jump", "13.43 m"], ["2014", "Asian Junior Championships", "Taipei City, Taiwan", "1st", "Long jump", "6.27 m"], ["2014", "Asian Junior Championships", "Taipei City, Taiwan", "2nd", "Triple jump", "13.62 m"], ["2014", "World Junior Championships", "Eugene, United States", "25th (q)", "Long jump", "5.72 m"], ["2014", "World Junior Championships", "Eugene, United States", "3rd", "Triple jump", "14.03 m"], ["2015", "World Championships", "Beijing, China", "21st (q)", "Triple jump", "13.52 m"], ["2016", "Asian Indoor Championships", "Hangzhou, China", "5th", "Triple jump", "13.14 m"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "34th (q)", "Triple jump", "13.30 m"]]}, "native_split": "test", "question": "In which year did the athlete achieve a higher position in the Triple jump event at the Asian Championships compared to the World Championships?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2013", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9c67f976517e940f4c621cc43685f9f1"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:a91f81d1472de78a9c78cef99cf9e92c", "source": "tablebench", "table_key": "bb9ab21d97aa7d32a8e47e71cae2c43a43995ac5", "grid": {"TD": 1, "LD": 0, "nrows": 18, "ncols": 12, "section_rows": [], "cells": [["draw", "artist", "song", "rank", "points", "a krajka", "gj leka", "b haxhia", "d tukiqi", "r magjistari", "gj xhuvani", "a skënderaj"], ["1", "manjola nallbani", "kjo botë merr frymë nga dashuria", "7", "27", "3", "4", "4", "7", "8", "1", "0"], ["2", "produkt 28", "30 sekonda", "15", "3", "0", "0", "0", "1", "1", "0", "1"], ["3", "eneida tarifa", "e para letër", "10", "11", "0", "1", "0", "0", "0", "7", "3"], ["4", "mariza ikonomi", "mall i tretur", "9", "20", "2", "3", "0", "3", "3", "3", "6"], ["5", "greta koçi", "natën të kërkova", "5", "35", "5", "5", "3", "6", "4", "8", "4"], ["6", "flaka krelani & doruntina disha", "jeta kërkon dashuri", "2", "57", "12", "12", "12", "12", "9", "0", "0"], ["7", "mira konçi & redon makashi", "nën një qiell", "6", "35", "6", "6", "6", "9", "6", "2", "0"], ["8", "kthjellu", "dhoma", "11", "9", "0", "0", "1", "0", "0", "0", "8"], ["9", "kozma dushi", "tatuazh në kujtesë", "16", "1", "1", "0", "0", "0", "0", "0", "0"], ["10", "devis xherahu", "endacaku", "17", "0", "0", "0", "0", "0", "0", "0", "0"], ["11", "teuta kurti", "qyteti i dashurisë", "14", "5", "0", "0", "5", "0", "0", "0", "0"], ["12", "samanta karavello", "pse u harrua dashuria", "8", "23", "4", "2", "2", "5", "0", "5", "5"], ["13", "juliana pasha", "një qiell të ri", "3", "54", "9", "9", "9", "4", "5", "9", "9"], ["14", "agim poshka", "kujt i them të dua", "12", "8", "0", "0", "0", "0", "2", "4", "2"], ["15", "jonida maliqi", "s'ka fajtor në dashuri", "4", "36", "0", "7", "7", "2", "7", "6", "7"], ["16", "olta boka", "zemrën e lamë peng", "1", "67", "7", "8", "8", "8", "12", "12", "12"], ["17", "rosela gjylbegu", "po lind një yll", "13", "8", "8", "0", "0", "0", "0", "0", "0"]]}, "native_split": "test", "question": "According to the table, what is the total points scored by the song \"qyteti i dashurisë\" ?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "5", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "a91f81d1472de78a9c78cef99cf9e92c"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:01b8f8e3ea00672844eb858c29324c9e", "source": "tablebench", "table_key": "78bedb9cb29f5c8654d272c46196ab1834ff7801", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Morocco", "Representing Morocco", "Representing Morocco", "Representing Morocco", "Representing Morocco", "Representing Morocco"], ["2003", "World Youth Championships", "Sherbrooke, Canada", "21st (h)", "800 m", "2:12.66"], ["2004", "World Junior Championships", "Grosseto, Italy", "9th", "800 m", "2:09.26"], ["2005", "World Cross Country Championships", "Saint-Galmier, France", "31st", "Junior race (6.153 km)", "22:42"], ["2005", "World Youth Championships", "Marrakech, Morocco", "4th", "800 m", "2:08.61"], ["2005", "Francophonie Games", "Niamey, Niger", "4th", "800 m", "2:09.64"], ["2005", "Francophonie Games", "Niamey, Niger", "3rd", "4 × 400 m relay", "3:42.48"], ["2006", "World Junior Championships", "Beijing, China", "12th (sf)", "800 m", "2:07.07"], ["2007", "World Cross Country Championships", "Mombasa, Kenya", "46th", "Junior race (6 km)", "24:01"], ["2007", "African Junior Championships", "Ouagadougou, Burkina Faso", "2nd", "800 m", "2:06.13"], ["2007", "African Junior Championships", "Ouagadougou, Burkina Faso", "3rd", "1500 m", "4:20.91"], ["2007", "Pan Arab Games", "Cairo, Egypt", "3rd", "800 m", "2:09.50"], ["2008", "African Championships", "Addis Ababa, Ethiopia", "4th", "800 m", "2:04.74"], ["2009", "Mediterranean Games", "Pescara, Italy", "2nd", "800 m", "2:00.91"], ["2009", "World Championships", "Berlin, Germany", "23rd (sf)", "800 m", "DNF"], ["2009", "Francophonie Games", "Beirut, Lebanon", "2nd", "800 metres", "2:02.76"], ["2009", "Francophonie Games", "Beirut, Lebanon", "3rd", "4 × 400 m relay", "3:37.72"], ["2010", "World Indoor Championships", "Doha, Qatar", "8th (h)", "800 m", "2:03.81"], ["2010", "African Championships", "Nairobi, Kenya", "8th", "800 m", "DNF"], ["2011", "World Championships", "Daegu, South Korea", "24th (sf)", "800 m", "DNF"], ["2012", "Olympic Games", "London, Great Britain", "11th (sf)", "800 m", "11th"], ["2013", "World Championships", "Moscow, Russia", "11th (sf)", "800 m", "11th"]]}, "native_split": "test", "question": "In which year did the athlete achieve their best position in the 800m event at the World Championships?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2013", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "01b8f8e3ea00672844eb858c29324c9e"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:410a163d2c0672ff88d17cab3cecc0c6", "source": "tablebench", "table_key": "30ab20000be30f4ac5016a8f5482da1406be16e3", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["Year", "Numer of Jamaicans\ngranted British\ncitizenship", "Naturalisation\nby residence", "Naturalisation\nby marriage", "Registration\nof a minor child", "Registration\nby other means"], ["1997", "732", "327", "279", "114", "12"], ["1998", "1,370", "571", "564", "221", "14"], ["1999", "1,437", "678", "526", "226", "7"], ["2000", "1,882", "927", "664", "281", "10"], ["2001", "2,070", "1,025", "710", "330", "0"], ["2002", "2,025", "1,035", "705", "285", "0"], ["2003", "2,795", "1,285", "985", "520", "5"], ["2004", "3,180", "1,415", "1,060", "640", "65"], ["2005", "3,515", "1,585", "1,080", "770", "80"], ["2006", "2,525", "1,110", "710", "655", "55"], ["2007", "3,165", "1,575", "825", "725", "45"], ["2008", "2,715", "1,275", "695", "700", "45"]]}, "native_split": "test", "question": "In 2001, how many Jamaicans were granted British citizenship through naturalization by marriage?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "710", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "410a163d2c0672ff88d17cab3cecc0c6"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:8b247fcb51d1c607ea0ba29eecad1bd2", "source": "tablebench", "table_key": "97a728f8a7778d0978d478cee67c21976151bfb9", "grid": {"TD": 1, "LD": 0, "nrows": 29, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Grenada", "Representing Grenada", "Representing Grenada", "Representing Grenada", "Representing Grenada", "Representing Grenada"], ["2007", "CARIFTA Games (U-17)", "Providenciales, Turks and Caicos Islands", "6th", "200 m", "22.10 (+1.2 m/s)"], ["2007", "CARIFTA Games (U-17)", "Providenciales, Turks and Caicos Islands", "1st", "400 m", "47.86 PB"], ["2007", "World Youth Championships", "Ostrava, Czech Republic", "2nd", "400 m", "46.96 PB"], ["2008", "CARIFTA Games (U-17)", "Basseterre, Saint Kitts and Nevis", "1st", "200 m", "21.38 (+2.0 m/s)"], ["2008", "CARIFTA Games (U-17)", "Basseterre, Saint Kitts and Nevis", "1st", "400 m", "47.87"], ["2008", "World Junior Championships", "Bydgoszcz, Poland", "2nd", "400 m", "45.70 PB"], ["2008", "Commonwealth Youth Games", "Pune, India", "1st", "400 m", "46.66 GR"], ["2009", "CARIFTA Games (U-20)", "Vieux Fort, Saint Lucia", "DQ (h1)", "200 m", "False start"], ["2009", "CARIFTA Games (U-20)", "Vieux Fort, Saint Lucia", "1st", "400 m", "45.45 PB GR"], ["2009", "CARIFTA Games (U-20)", "Vieux Fort, Saint Lucia", "DQ (h1)", "4 × 100 m relay", "Out of zone"], ["2009", "CARIFTA Games (U-20)", "Vieux Fort, Saint Lucia", "3rd", "4 × 400 m relay", "3:11.93 PB"], ["2009", "World Youth Championships", "Brixen, Italy", "1st", "200 m", "21.05 (−0.9 m/s) PB"], ["2009", "World Youth Championships", "Brixen, Italy", "1st", "400 m", "45.24 PB CR"], ["2009", "Pan American Junior Championships", "Port of Spain, Trinidad and Tobago", "1st", "400 m", "45.43"], ["2009", "Pan American Junior Championships", "Port of Spain, Trinidad and Tobago", "5th", "4 × 400 m relay", "3:11.91 PB"], ["2010", "CARIFTA Games (U-20)", "George Town, Cayman Islands", "1st", "200 m", "20.76 (+0.8 m/s) SB"], ["2010", "CARIFTA Games (U-20)", "George Town, Cayman Islands", "1st", "400 m", "45.02 PB GR"], ["2010", "World Junior Championships", "Moncton, Canada", "1st", "400 m", "45.89"], ["2011", "CAC Championships", "Mayagüez, Puerto Rico", "5th", "4 × 400 m relay", "3:04.27 NR PB"], ["2011", "Pan American Junior Championships", "Miramar, Florida", "1st", "200 m", "20.53 w (+2.2 m/s)"], ["2011", "World Championships", "Daegu, South Korea", "1st", "400 m", "44.60 PB"], ["2012", "World Indoor Championships", "Istanbul, Turkey", "6th", "400 m", "46.21"], ["2012", "Olympic Games", "London, England", "1st", "400 m", "43.94 WL NR PB"], ["2013", "World Championships", "Moscow, Russia", "7th", "400 m", "44.99"], ["2014", "Commonwealth Games", "Glasgow, Scotland", "1st", "400 m", "44.24 GR"], ["2015", "World Championships", "Beijing, China", "3rd", "400 m", "43.78 SB"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "2nd", "400 m", "43.76 SB"]]}, "native_split": "test", "question": "In which year did the athlete win a gold medal in the 400m event at the CARIFTA Games (U-20) with a personal best time?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2009,2010", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8b247fcb51d1c607ea0ba29eecad1bd2"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:b706776a23c7b8fc4d8036047a48890e", "source": "tablebench", "table_key": "786f762cd0a2168a8ddc138c51b197bce0233b9e", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 9, "section_rows": [], "cells": [["position", "team", "played", "wins", "draws", "losses", "scored", "conceded", "points"], ["1", "cerro porteño", "9", "5", "2", "2", "14", "7", "17"], ["2", "libertad", "9", "4", "4", "1", "12", "4", "16"], ["3", "12 de octubre", "9", "5", "1", "3", "15", "10", "16"], ["4", "cerro corá", "9", "4", "2", "3", "15", "14", "14"], ["5", "san lorenzo", "9", "4", "1", "4", "11", "11", "13"], ["6", "sportivo luqueño", "9", "3", "4", "2", "11", "12", "13"], ["7", "guaraní", "9", "3", "1", "5", "6", "9", "10"], ["8", "sol de américa", "9", "2", "3", "4", "11", "16", "9"], ["9", "atl colegiales", "9", "2", "3", "4", "6", "11", "9"]]}, "native_split": "test", "question": "Which team is currently in 3rd position in the league?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "12 de octubre", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b706776a23c7b8fc4d8036047a48890e"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:1b9e6880bae6250d652f23b7ae3c9102", "source": "tablebench", "table_key": "8b2e68e2f3482e47544f0b4f2a8530c7ea4b115b", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 6, "section_rows": [], "cells": [["Unnamed: 0", "1948", "1952", "1956", "1960", "1964"], ["all voters", "50.0", "45.0", "42", "50", "61"], ["White", "50.0", "43.0", "41", "49", "59"], ["Black", "50.0", "79.0", "61", "68", "94"], ["College educated", "22.0", "34.0", "31", "39", "52"], ["High School educated", "51.0", "45.0", "42", "52", "62"], ["Grade School educated", "64.0", "52.0", "50", "55", "66"], ["Professional & Business", "19.0", "36.0", "32", "42", "54"], ["White Collar", "47.0", "40.0", "37", "48", "57"], ["Manual worker", "66.0", "55.0", "50", "60", "71"], ["Farmer", "60.0", "33.0", "46", "48", "53"], ["Union member", "76.0", "None", "51", "62", "77"], ["Not union", "42.0", "None", "35", "44", "56"], ["Protestant", "43.0", "37.0", "37", "38", "55"], ["Catholic", "62.0", "56.0", "51", "78", "76"], ["Republican", "None", "8.0", "4", "5", "20"], ["Independent", "None", "35.0", "30", "43", "56"], ["Democrat", "None", "77.0", "85", "84", "87"], ["East", "48.0", "45.0", "40", "53", "68"], ["Midwest", "50.0", "42.0", "41", "48", "61"], ["West", "49.0", "42.0", "43", "49", "60"], ["South", "53.0", "51.0", "49", "51", "52"]]}, "native_split": "test", "question": "What percentage of Black voters voted in 1960?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "68", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "1b9e6880bae6250d652f23b7ae3c9102"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:69837e67a1ab18c4f912f97bf9c714bd", "source": "tablebench", "table_key": "dccbe08e70a2a36db3c34e7bb6c0fec618d9fb57", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 6, "section_rows": [], "cells": [["year", "men 's singles", "women 's singles", "men 's doubles", "women 's doubles", "mixed doubles"], ["1993", "jim laugesen", "mette sørensen", "neil cottrill john quinn", "nadezhda chervyakova marina yakusheva", "john quinn nicola beck"], ["1994", "henrik sörensen", "irina serova", "henrik sörensen claus simonsen", "lene sörensen mette sørensen", "jürgen koch irina serova"], ["1995", "thomas soegaard", "elena rybkina", "thomas stavngaard janek roos", "michelle rasmussen mette sørensen", "janek roos pernille harder"], ["1996", "daniel ericsson", "tanja berg", "johan tholinsson henrik andersson", "ann - lou jørgensen christina sörensen", "jonas rasmussen ann - lou jørgensen"], ["1997", "martin hagberg", "anne gibson", "james anderson ian sullivan", "rebeka pantaney gail emms", "ian sulivan gail emms"], ["1998", "robert nock", "ella karachkova", "graham hurrell paul jeffrey", "lorraine cole tracey dineen", "anthony clark lorraine cole"], ["1999", "robert nock", "katja michalowsky", "svetoslav stojanov michal popov", "liza parker suzanne rayappan", "ola molin johanna persson"], ["2000", "gerben bruystens", "christina b sörensen", "thomas hovgaard jesper mikla", "britta andersen lene mork", "mathias boe britta andersen"], ["2001", "bobby milroy", "rebecca panteney", "michael popov manuel dubrulle", "nadiezda kostiuczyk kamila augustyn", "kristian roebuck natalie munt"], ["2002", "przemysław wacha", "sara persson", "svetoslav stoyanov vincent laigle", "johanna persson elin berglom", "andrey konakh nadiezda kostiuczyk"], ["2003", "michael christensen", "petya nedelcheva", "michael popov manuel dubrulle", "petya nedelcheva nely boteva", "mike beres jody patrick"], ["2004", "per - henrik croona", "katja michalowsky", "mike beres william milroy", "britta andersen mie schjott kristensen", "jesper thomsen britta andersen"], ["2005", "przemysław wacha", "susan hughes", "chris langridge chris tonks", "nadiezda kostiuczyk kamila augustyn", "henri hurskainen johanna persson"], ["2006", "jan o jorgensen", "ragna ingolfsdottir", "robert adcock robin middleton", "mie schjott - kristensen christinna pedersen", "liza parker robin middleton"], ["2007", "arvind bhat", "rachel van cutsen", "kasper henriksen rasmus bonde", "mie schjott - kristensen christinna pedersen", "rasmus bonde christinna pedersen"], ["2008", "chetan anand", "ella diehl", "kasper henriksen christian skovgaard", "helle nielsen marie roepke", "rasmus bonde helle nielsen"], ["2009", "petr koukal", "trupti murgunde", "mads conrad - petersen mads pieler kolding", "maria helsbol anne skelbaek", "viki indra okvana gustiani megawati"], ["2010", "ajay jayaram", "karina jörgensen", "chris langridge robin middleton", "selena piek iris tabeling", "anders skaarup rasmussen anne skelbaek"], ["2011", "przemyslaw wacha", "kristina gavnholt", "adam cwalina michal logosz", "valeria sorokina nina vislova", "alexandr nikolaenko nina vislova"], ["2012", "joachim persson", "kirsty gilmour", "chris langridge peter mills", "heather olver kate robertshaw", "chris langridge heather olver"]]}, "native_split": "test", "question": "Who is the only player to have won both the men's singles and men's doubles titles in the same year, and what is that year?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "henrik sörensen, 1994", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "69837e67a1ab18c4f912f97bf9c714bd"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:9280930f516f9e7e740df6336b2c327a", "source": "tablebench", "table_key": "32476da4fb386b183b9b7ddaec237aee2ef9cdd9", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 8, "section_rows": [], "cells": [["player", "matches", "innings", "runs", "average", "highest score", "100s", "50s"], ["lionel palairet", "10", "19", "560", "31.11", "100", "1", "5"], ["john challen", "9", "16", "354", "25.28", "89", "0", "2"], ["richard palairet", "10", "17", "266", "19.0", "74", "0", "1"], ["herbie hewett", "12", "22", "388", "18.47", "65", "0", "2"], ["sammy woods", "11", "19", "330", "18.33", "50", "0", "1"], ["bill roe", "7", "12", "168", "15.27", "36", "0", "0"], ["crescens robinson", "11", "17", "196", "14.0", "55", "0", "1"], ["vernon hill", "9", "15", "184", "12.26", "31", "0", "0"], ["george nichols", "12", "21", "216", "10.28", "37", "0", "0"], ["ted tyler", "12", "20", "168", "9.88", "62", "0", "1"]]}, "native_split": "test", "question": "What is the highest score achieved by Lionel Palairet in his cricket career?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "100", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9280930f516f9e7e740df6336b2c327a"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:7b6040473988da74ffaf42edf90be4a9", "source": "tablebench", "table_key": "a561c4545253a413a1b926e0848d34f58792be45", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["1992", "CARIFTA Games (U20)", "Nassau, Bahamas", "1st", "Discus throw", "37.86 m"], ["1992", "CARIFTA Games (U20)", "Nassau, Bahamas", "3rd", "Javelin throw", "36.37 m"], ["1999", "Central American and Caribbean Championships", "Bridgetown, Barbados", "2nd", "Javelin throw", "54.24 m"], ["2000", "Olympic Games", "Sydney, Australia", "21st (q)", "Javelin throw", "56.36 m"], ["2005", "Central American and Caribbean Championships", "Nassau, Bahamas", "2nd", "Javelin throw", "61.10 m"], ["2005", "World Championships", "Helsinki, Finland", "14th (q)", "Javelin throw", "58.49 m"], ["2006", "Commonwealth Games", "Melbourne, Australia", "3rd", "Javelin throw", "58.27 m"], ["2006", "Central American and Caribbean Games", "Cartagena, Colombia", "4th", "Javelin throw", "56.82 m"], ["2008", "Olympic Games", "Beijing, China", "34th (q)", "Javelin throw", "55.51 m"], ["2011", "Pan American Games", "Guadalajara, Mexico", "7th", "Javelin throw", "51.40 m"]]}, "native_split": "test", "question": "In which year did the athlete achieve their highest ranking in the Javelin throw event at the Olympic Games?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2000", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "7b6040473988da74ffaf42edf90be4a9"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:615fe8039d2c84220b9f383b5e287a50", "source": "tablebench", "table_key": "b3748f9b0ce72ffa45220f64612ac7b1e4b2cda0", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 8, "section_rows": [], "cells": [["Unnamed: 0", "Average population (x 1000)", "Live births", "Deaths", "Natural change", "Crude birth rate (per 1000)", "Crude death rate (per 1000)", "Natural change (per 1000)"], ["1970", "38", "761", "299", "462", "20.0", "7.9", "12.2"], ["1975", "42", "857", "317", "540", "20.4", "7.5", "12.9"], ["1980", "46", "996", "333", "663", "21.7", "7.2", "14.4"], ["1985", "51", "1 104", "370", "734", "21.6", "7.3", "14.4"], ["1990", "51", "842", "360", "482", "16.4", "7.0", "9.4"], ["1991", "50", "789", "335", "454", "15.8", "6.7", "9.1"], ["1992", "48", "692", "401", "291", "14.4", "8.3", "6.0"], ["1993", "46", "617", "448", "169", "13.4", "9.7", "3.7"], ["1994", "44", "585", "518", "67", "13.3", "11.8", "1.5"], ["1995", "43", "537", "501", "36", "12.6", "11.8", "0.8"], ["1996", "42", "486", "441", "45", "11.7", "10.6", "1.1"], ["1997", "41", "483", "374", "109", "11.9", "9.2", "2.7"], ["1998", "40", "498", "368", "130", "12.6", "9.3", "3.3"], ["1999", "39", "448", "376", "72", "11.6", "9.7", "1.9"], ["2000", "38", "460", "438", "22", "12.0", "11.4", "0.6"], ["2001", "39", "562", "438", "124", "14.5", "11.3", "3.2"], ["2002", "39", "608", "397", "211", "15.5", "10.1", "5.4"], ["2003", "39", "625", "386", "239", "15.9", "9.8", "6.1"], ["2004", "39", "637", "345", "292", "16.5", "8.9", "7.6"], ["2005", "38", "548", "369", "179", "14.5", "9.7", "4.7"], ["2006", "37", "540", "347", "193", "14.5", "9.3", "5.2"]]}, "native_split": "test", "question": "According to the table, what was the crude birth rate (per 1000) in 1995?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "12.6", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "615fe8039d2c84220b9f383b5e287a50"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:5921991ef07b397dfd587a30c770faea", "source": "tablebench", "table_key": "c637b79956a389da1a4139fdc2e3133dbef4988f", "grid": {"TD": 1, "LD": 0, "nrows": 27, "ncols": 6, "section_rows": [], "cells": [["year", "delegate", "hometown", "pageant", "result", "other awards"], ["1971", "nelia sancho", "no information available", "queen of the pacific", "winner", "none"], ["1971", "milagros gutierrez", "no information available", "miss charming international", "second runner - up", "none"], ["1972", "maria isabel seva", "no information available", "miss charming international", "did not place", "none"], ["1989", "maria rita apostol", "no information available", "miss flower queen", "did not place", "none"], ["1992", "sharmaine rama gutierrez", "manila , metro manila", "elite model look", "did not place", "none"], ["1993", "anna maria gonzalez", "no information available", "elite model look", "did not place", "none"], ["1995", "rollen richelle caralde", "no information available", "elite model look", "did not place", "none"], ["1996", "ailleen marfori damiles", "las piñas , metro manila", "international folklore beauty pageant", "top 5 finalist", "miss photogenic"], ["1997", "joanne zapanta santos", "san fernando , pampanga", "miss tourism international", "winner", "none"], ["2000", "rachel muyot soriano", "no information available", "miss tourism world", "second runner - up", "best in long gown"], ["2001", "maricar manalaysay balagtas", "bulacan", "miss globe international", "winner", "best national costume"], ["2001", "michelle cueva reyes", "caloocan city , metro manila", "miss tourism international", "winner", "best national costume"], ["2001", "zorayda ruth blanco andam", "baguio city", "miss tourism world", "finalist", "miss tourism world asia"], ["2001", "joanna maria mijares peñaloza", "mandaluyong city , metro manila", "miss internet www", "did not place", "face of the net"], ["2002", "kristine reyes alzar", "lipa , batangas", "miss tourism international", "winner", "best national costume"], ["2002", "karen loren medrano agustin", "manila , metro manila", "miss globe international", "fifth runner - up", "best in swimsuit"], ["2002", "michelle cueva reyes", "caloocan city , metro manila", "miss tourism world", "winner", "best national costume"], ["2002", "margaret - ann awitan bayot", "antipolo , rizal", "miss internet www", "second runner - up", "none"], ["2003", "noella mae evangelista", "iligan city", "queen of tourism international", "winner", "best national costume"], ["2004", "karen loren medrano agustin", "manila , metro manila", "world coffee queen", "second runner - up", "none"], ["2004", "margaret ann awitan bayot", "antipolo , rizal", "miss maja mundial", "first runner - up / virreina", "none"], ["2005", "jhezarie games javier", "manila , metro manila", "miss asean", "winner", "none"], ["2006", "carlene ang aguilar", "quezon city , metro manila", "miss internet www", "winner", "none"], ["2009", "april love antolo jordan", "manila , metro manila", "beauty of the world", "winner", "none"], ["2010", "mariella castillo", "mabini , batangas", "miss global teen", "top 12 semi - finalist", "teen queen of asia and oceania"], ["2011", "czarina catherine gatbonton", "malolos , bulacan", "miss humanity international", "second runner - up", "none"]]}, "native_split": "test", "question": "How many delegates from Metro Manila won a pageant title?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "5921991ef07b397dfd587a30c770faea"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:3e8c9ae6880c285746e8344707583b81", "source": "tablebench", "table_key": "13b5250dd5600d2a2a9df05da33d6c0070ac2ffb", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["rank", "nation", "gold", "silver", "bronze", "total"], ["1", "united states", "50", "41", "32", "123"], ["2", "great britain", "18", "23", "20", "61"], ["3", "italy", "14", "15", "16", "45"], ["4", "australia", "12", "11", "7", "30"], ["5", "rhodesia", "10", "5", "2", "17"], ["6", "south africa", "8", "8", "3", "19"], ["7", "israel", "7", "3", "11", "21"], ["8", "argentina", "6", "15", "16", "37"], ["9", "west germany", "5", "2", "5", "12"], ["10", "netherlands", "4", "6", "4", "14"]]}, "native_split": "test", "question": "According to the table, which nation won 18 gold medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "great britain", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3e8c9ae6880c285746e8344707583b81"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:ad90ad414ff991cd2c1aed8154091536", "source": "tablebench", "table_key": "56fece95a7df63fbb18276dc63a60d35707da18f", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Algeria", "Representing Algeria", "Representing Algeria", "Representing Algeria", "Representing Algeria", "Representing Algeria"], ["2001", "World Youth Championships", "Debrecen, Hungary", "26th", "10,000 m walk", "48:40.35"], ["2004", "World Race Walking Cup", "Naumburg, Germany", "–", "20 km walk", "DQ"], ["2006", "African Championships", "Bambous, Mauritius", "6th", "20 km walk", "1:29:34"], ["2007", "All-Africa Games", "Algiers, Algeria", "3rd", "20 km walk", "1:25:12"], ["2007", "Pan Arab Games", "Cairo, Egypt", "3rd", "20,000 m walk", "1:43:35.8"], ["2008", "African Championships", "Addis Ababa, Ethiopia", "1st", "20 km walk", "1:22:55 (CR)"], ["2008", "Olympic Games", "Beijing, China", "48th", "20 km walk", "1:32:21"], ["2009", "Universiade", "Belgrade, Serbia", "15th", "20 km walk", "1:26:21"], ["2010", "African Championships", "Nairobi, Kenya", "5th", "20 km walk", "1:24:53"], ["2012", "African Championships", "Addis Ababa, Ethiopia", "3rd", "20 km walk", "?"], ["2014", "African Championships", "Marrakech, Morocco", "3rd", "20 km walk", "1:27:48"], ["2015", "African Games", "Brazzaville, Republic of the Congo", "–", "20 km walk", "DNF"], ["2016", "African Championships", "Durban, South Africa", "7th", "20 km walk", "1:26:17"], ["2018", "African Championships", "Asaba, Nigeria", "6th", "20 km walk", "1:28.38"]]}, "native_split": "test", "question": "In which year did the athlete achieve their personal best time in the 20 km walk event at the African Championships?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2008", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ad90ad414ff991cd2c1aed8154091536"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:8784e31776b33c2a8c9988602a50dabc", "source": "tablebench", "table_key": "eada49ebaa17dd6471c80993d08b3f5407067eba", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 11, "section_rows": [], "cells": [["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["maesteg rfc", "22", "2", "1", "615", "271", "78", "24", "12", "0", "92"], ["waunarlwydd rfc", "22", "1", "7", "594", "359", "73", "38", "10", "5", "73"], ["bp llandarcy rfc", "22", "1", "7", "376", "320", "43", "36", "3", "5", "66"], ["kidwelly rfc", "22", "0", "9", "558", "393", "68", "39", "6", "6", "64"], ["aberavon quins rfc", "22", "0", "9", "449", "424", "56", "45", "6", "3", "61"], ["ammanford rfc", "22", "1", "10", "409", "348", "45", "33", "4", "8", "58"], ["loughor rfc", "22", "1", "11", "427", "479", "47", "60", "5", "4", "51"], ["aberystwyth rfc", "22", "0", "12", "390", "509", "46", "71", "5", "4", "49"], ["pontyberem rfc", "22", "0", "12", "353", "520", "35", "67", "4", "3", "47"], ["mumbles rfc", "22", "1", "14", "372", "471", "51", "55", "5", "4", "39"], ["pencoed rfc", "22", "0", "19", "321", "505", "34", "62", "0", "10", "22"], ["dunvant rfc", "22", "1", "17", "324", "589", "33", "79", "0", "2", "20"]]}, "native_split": "test", "question": "How many games did Maesteg RFC play in the season?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "22", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8784e31776b33c2a8c9988602a50dabc"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:d494234ecc77e5ad04517a99c500b01b", "source": "tablebench", "table_key": "2ef6ea41bd1075df63b48e3398af6ca626d29826", "grid": {"TD": 1, "LD": 0, "nrows": 28, "ncols": 6, "section_rows": [], "cells": [["year", "mens singles", "womens singles", "mens doubles", "womens doubles", "mixed doubles"], ["1985", "jeliazko valkov", "diana koleva", "ilko orechov nanko ertchopov", "diana koleva zlatka valkanova", "jeliazko valkov dobrinka peneva"], ["1986", "jeliazko valkov", "diana koleva", "jeliazko valkov dinko dukov", "diana koleva petia borisova", "ilko okreshkov elena velinova"], ["1987", "stanimir boitchinov", "diana koleva", "jeliazko valkov dinko dukov", "diana koleva diana filipova", "jeliazko valkov gabriela spasova"], ["1988", "jeliazko valkov", "diana koleva", "jeliazko valkov dinko dukov", "diana koleva emilia dimitrova", "jeliazko valkov irina dimitrova"], ["1989", "stanimir boitchinov", "diana koleva", "jeliazko valkov dinko dukov", "diana koleva emilia dimitrova", "jeliazko valkov diana filipova"], ["1990", "stoyan ivantchev", "diana koleva", "slantcezar tzankov anatoliy skripko", "diana koleva emilia dimitrova", "anatoliy skripko diana filipova"], ["1991", "stoyan ivantchev", "victoria hristova", "stoyan ivantchev anatoliy skripko", "diana koleva emilia dimitrova", "jeliazko valkov emilia dimitrova"], ["1992", "jassen borissov", "diana koleva", "jeliazko valkov sibin atanasov", "diana koleva diana filipova", "slantchezar tzankov diana filipova"], ["1993", "todor velkov", "dimitrinka dimitrova", "boris kesov anatoliy skripko", "victoria hristova nelly nedjalkova", "svetoslav stoyanov emilia dimitrova"], ["1994", "mihail popov", "victoria hristova", "svetoslav stoyanov mihail popov", "raina tzvetkova emilia dimitrova", "svetoslav stoyanov raina tzvetkova"], ["1995", "todor velkov", "neli nedialkova", "svetoslav stoyanov mihail popov", "raina tzvetkoa victoria hristova", "svetoslav stoyanov raina tzvetkova"], ["1996", "mihail popov", "victoria hristova", "svetoslav stoyanov mihail popov", "victoria hristova neli nedialkova", "svetoslav stoyanov raina tzvetkova"], ["1997", "boris kessov", "raina tzvetkova", "svetoslav stoyanov mihail popov", "victoria hristova dobrinka smilianova", "svetoslav stoyanov raina tzvetkova"], ["1998", "mihail popov", "victoria hristova", "svetoslav stoyanov mihail popov", "victoria hristova raina tzvetkova", "svetoslav stoyanov raina tzvetkova"], ["1999", "boris kessov", "neli boteva", "boris kessov tzvetozar kolev", "raina tzvetkova petya nedelcheva", "konstantin dobrev petya nedelcheva"], ["2000", "luben panov", "petya nedelcheva", "konstantin dobrev luben panov", "petya nedelcheva neli boteva", "konstantin dobrev petya nedelcheva"], ["2001", "konstantin dobrev", "petya nedelcheva", "konstantin dobrev luben panov", "petya nedelcheva maya ivanova", "konstantin dobrev petya nedelcheva"], ["2002", "boris kessov", "petya nedelcheva", "konstantin dobrev georgi petrov", "petya nedelcheva nely boteva", "boris kessov nely boteva"], ["2003", "georgi petrov", "nely boteva", "julian hristov boris kessov", "petya nedelcheva diana koleva", "julian hristov diana dimova"], ["2004", "yulian hristov", "petya nedelcheva", "stilian makarski bladimir metodiev", "petya nedelcheva nely boteva", "vladimir metodiev petya nedelcheva"], ["2005", "kostantin dobrev", "petya nedelcheva", "konstantin dobrev georgi petrov", "petya nedelcheva maya lvanova", "vladimir metodiev petya nedelcheva"], ["2006", "georgi petrov", "petya nedelcheva", "georgi petrov blagovest kisiov", "petya nedelcheva diana dimova", "vladimir metodiev petya nedelcheva"], ["2007", "georgi petrov", "petya nedelcheva", "vladimir metodiev stilian makarski", "petya nedelcheva diana dimova", "vladimir metodiev diana dimova"], ["2008", "stilian makarski", "petya nedelcheva", "vladimir metodiev krasimir yankov", "petya nedelcheva diana dimova", "stilian makarski diana dimova"], ["2009", "krasimir yankov", "petya nedelcheva", "vladimir metodiev krasimir yankov", "petya nedelcheva dimitria popstoykova", "stilian makarski diana dimova"], ["2010", "stilian makarski", "petya nedelcheva", "stilian makarski peyo boichinov", "petya nedelcheva diana dimova", "stilian makarski diana dimova"], ["2011", "peyo boichinov", "petya nedelcheva", "stilian makarski peyo boichinov", "petya nedelcheva diana dimova", "stilian makarski diana dimova"]]}, "native_split": "test", "question": "Which player, who has won the men's singles title and also won the mixed doubles title at least twice in the same year?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "jeliazko valkov, stilian makarski", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d494234ecc77e5ad04517a99c500b01b"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:db5d51a82497e56cdd5c4b55ba02f07b", "source": "tablebench", "table_key": "e25db0921e7d21dfd00e642bfa5ea0b64d8b4d4e", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1", "United States (USA)", "25", "16", "7", "48"], ["2", "Canada (CAN)", "5", "4", "7", "16"], ["3", "Jamaica (JAM)", "4", "3", "4", "11"], ["4", "Cuba (CUB)", "3", "8", "4", "15"], ["5", "Mexico (MEX)", "0", "3", "3", "6"], ["6", "Brazil (BRA)", "0", "2", "1", "3"], ["7", "Puerto Rico (PUR)", "0", "1", "1", "2"], ["8", "Colombia (COL)", "0", "0", "3", "3"], ["9", "Peru (PER)", "0", "0", "2", "2"], ["9", "Trinidad and Tobago (TRI)", "0", "0", "2", "2"], ["11", "Chile (CHI)", "0", "0", "1", "1"], ["11", "Uruguay (URU)", "0", "0", "1", "1"], ["11", "Venezuela (VEN)", "0", "0", "1", "1"]]}, "native_split": "test", "question": "According to the table, which nation won 4 gold medals and 3 silver medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Jamaica (JAM)", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "db5d51a82497e56cdd5c4b55ba02f07b"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:2188238068289d7a4408ca02a059bc48", "source": "tablebench", "table_key": "ae281b00d0df970cceac2f8afa0d2a448c989e3e", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["year", "competition", "venue", "position", "event", "notes"], ["2006", "nacac u23 championships", "santo domingo , dominican republic", "3rd", "4100 m relay", "39.98"], ["2007", "nacac championships", "san salvador , el salvador", "1st", "100 m", "10.32"], ["2007", "nacac championships", "san salvador , el salvador", "3rd", "4100 m relay", "39.92"], ["2007", "pan american games", "rio de janeiro , brazil", "4th (h)", "4100 m relay", "39.02"], ["2007", "world championships", "osaka , japan", "31st (h)", "100 m", "10.44"], ["2008", "central american and caribbean championships", "cali , colombia", "1st", "4100 m relay", "38.54"], ["2008", "olympic games", "beijing , china", "2nd", "100 m", "9.89"], ["2008", "olympic games", "beijing , china", "2nd", "4100 m relay", "38.06"], ["2009", "world championships", "berlin , germany", "5th", "100 m", "9.93"], ["2009", "world championships", "berlin , germany", "2nd", "4100 m relay", "37.62"], ["2011", "world championships", "daegu , south korea", "10th (sf)", "100 m", "10.2"], ["2011", "world championships", "daegu , south korea", "6th", "4100 m relay", "39.01"], ["2012", "olympic games", "london , united kingdom", "7th", "100 m", "9.98"], ["2012", "olympic games", "london , united kingdom", "3rd", "4100 m relay", "38.12"]]}, "native_split": "test", "question": "In which year did the athlete win a gold medal in the 100 m event?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2007", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "2188238068289d7a4408ca02a059bc48"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:95f0dcfec48507cc665cc640daf4fe9d", "source": "tablebench", "table_key": "98ff706c7630633368687e68f8b9f5ac8cdda63a", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 6, "section_rows": [], "cells": [["year", "men 's singles", "women 's singles", "men 's doubles", "women 's doubles", "mixed doubles"], ["1975", "victor yusim", "no competition", "victor yusim michael schneidman", "no competition", "no competition"], ["1976", "michael schneidman", "tineke hof", "victor yusim michael schneidman", "tineke hof devora geffen", "no competition"], ["1977", "victor yusim", "eva unglick", "victor yusim michael schneidman", "eva unglick chaya grunstein", "no competition"], ["1978", "victor yusim", "chaya grunstein", "victor yusim michael schneidman", "chaya grunstein carole silman", "michael rappaport carole silman"], ["1979", "victor yusim", "eva unglick", "victor yusim michael schneidman", "eva unglick chaya grunstein", "nissim duk eva unglick"], ["1980", "yitzhak serrouya", "elka kalb", "nissim duk yitzhak serrouya", "elka kalb irit ben shushan", "michael rappaport eva unglick"], ["1981", "johann ratheyser", "adelhid losek", "johann rathyser gerard hofegger", "eva unglick irit ben shushan", "johann ratheyser adelheid losek"], ["1982", "andrew downes", "lisa salmon", "david spurling stuart spurling", "lisa salmon j downes", "david spurling h blake"], ["1983 1989", "no competition", "no competition", "no competition", "no competition", "no competition"], ["1990", "stephane renault", "christelle mol", "ricardo fernandes marco vasconcelos", "christelle mol virginie delvingt", "stephane renault elodie mansuy"], ["1991 1997", "no competition", "no competition", "no competition", "no competition", "no competition"], ["1998", "aivaras kvedarauskas", "svetlana zilberman", "aivaras kvedarauskas nir yusim", "svetlana zilberman diana koleva", "leon pugatch svetlana zilberrman"], ["1999 2005", "no competition", "no competition", "no competition", "no competition", "no competition"], ["2006", "petr koukal", "maja tvrdy", "luka petric mateuz srekl", "no competition", "luka petric maja tvrdy"], ["2007", "sho sasaki", "tracey hallam", "jochen cassel thomas tesche", "no competition", "valeriy atrashenkov elena prus"]]}, "native_split": "test", "question": "Which player has won the most titles in men's singles and men's doubles combined, considering only the years between 1975 and 1982?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "victor yusim", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "95f0dcfec48507cc665cc640daf4fe9d"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:d7b545735f844944b02b05fe0343cb44", "source": "tablebench", "table_key": "fe429db5886eb0ed9808cd97393eedbda630bb0d", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 11, "section_rows": [], "cells": [["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["club", "played", "drawn", "lost", "points for", "points against", "tries for", "tries against", "try bonus", "losing bonus", "points"], ["llandeilo rfc", "22", "1", "0", "917", "119", "136", "14", "19", "0", "105"], ["brynamman rfc", "22", "1", "2", "821", "210", "116", "27", "16", "2", "96"], ["tenby united rfc", "22", "0", "8", "562", "461", "78", "61", "10", "1", "67"], ["pembroke dock harlequins rfc", "22", "0", "8", "423", "351", "56", "40", "7", "3", "66"], ["pontarddulais rfc", "22", "1", "9", "550", "503", "79", "68", "11", "5", "66"], ["betws rfc", "22", "1", "9", "528", "440", "72", "63", "9", "0", "59"], ["trimsaran rfc", "22", "0", "12", "471", "540", "68", "77", "7", "1", "48"], ["pembroke rfc", "22", "0", "13", "467", "500", "69", "66", "8", "4", "48"], ["burry port rfc", "22", "1", "14", "373", "688", "47", "99", "3", "2", "31"], ["hendy rfc", "22", "0", "17", "292", "707", "38", "109", "1", "6", "27"], ["tycroes rfc", "22", "0", "18", "267", "645", "35", "89", "3", "3", "18"], ["cwmgors rfc", "22", "1", "19", "211", "718", "28", "109", "2", "3", "15"]]}, "native_split": "test", "question": "According to the table, how many tries did Llandeilo RFC score in the season?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "136", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d7b545735f844944b02b05fe0343cb44"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:6197fb264f5bffbbaadd753cde55c2e5", "source": "tablebench", "table_key": "653a2f38f813ec11b7e616f9fa79e228d7b70b29", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1", "China", "63", "46", "32", "141"], ["2", "Great Britain", "35", "30", "29", "94"], ["3", "Canada", "28", "19", "25", "72"], ["4", "United States", "27", "22", "39", "88"], ["5", "Australia", "26", "38", "36", "100"], ["6", "Ukraine", "24", "12", "19", "55"], ["7", "Spain", "20", "27", "24", "71"], ["8", "Germany", "19", "28", "31", "78"], ["9", "France", "18", "26", "30", "74"], ["10", "Japan", "17", "16", "20", "53"]]}, "native_split": "test", "question": "Which nation won 30 silver medals in the Olympic Games?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Great Britain", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "6197fb264f5bffbbaadd753cde55c2e5"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:f100d3bd641b61a7b65d98cf81586752", "source": "tablebench", "table_key": "cd314bee1f029963f5deed498522ccd02c3abc73", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Russia", "Representing Russia", "Representing Russia", "Representing Russia", "Representing Russia", "Representing Russia"], ["1996", "World Junior Championships", "Sydney, Australia", "18th (h)", "3000m", "9:42.99"], ["1996", "World Junior Championships", "Sydney, Australia", "12th", "5000m", "16:32.51"], ["1997", "European U23 Championships", "Turku, Finland", "7th", "5000m", "15:58.60"], ["1997", "European U23 Championships", "Turku, Finland", "7th", "10,000m", "33:48.43"], ["2003", "World Indoor Championships", "Birmingham, England", "6th", "3000 m", "8:50.62"], ["2003", "World Championships", "Paris, France", "6th", "10,000 m", "30:26.20"], ["2004", "World Indoor Championships", "Budapest, Hungary", "11th", "3000 m", "9:17.15"], ["2005", "World Championships", "Helsinki, Finland", "8th", "10,000 m", "30:33.75"], ["2005", "World Half Marathon Championships", "Edmonton, Canada", "4th", "Half marathon", "1:10:34"], ["2006", "European Championships", "Gothenburg, Sweden", "4th", "10,000 m", "30:35.90"], ["2008", "Rome City Marathon", "Rome, Italy", "1st", "Marathon", "2:22:53"], ["2008", "Olympic Games", "Beijing, PR China", "—", "Marathon", "DNF"]]}, "native_split": "test", "question": "What is the event in which the athlete achieved a position of 4th in 2005, and what was the corresponding venue?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Half marathon, Edmonton, Canada", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f100d3bd641b61a7b65d98cf81586752"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:17df1b57444c60eb8a376c944f8078d4", "source": "tablebench", "table_key": "24278f124e032e1ee46051fb541bce32a08a6d06", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 7, "section_rows": [], "cells": [["Year", "Winners", "Score", "Score.1", "Runners-up", "Venue", "Winning Captain"], ["2000", "Meath", "5-14 (29)", "2-10 (16)", "Carlow", "Cusack Park", "None"], ["2001", "Kerry", "4-14 (22)", "3-10 (19)", "Westmeath", "Croke Park", "Michael 'Boxer' Slattery"], ["2002", "Laois", "1-20 (23)", "2-14 (20)", "Antrim", "Semple Stadium", "John Lyons"], ["2003", "Antrim", "3-18 (27)", "3-12 (21)", "Kerry", "Croke Park", "None"], ["2004", "Down", "5-15 (30)", "3-7 (16)", "Westmeath", "Gaelic Grounds", "Simon Wilson"], ["2005", "Offaly", "6-21 (39)", "4-7 (19)", "Carlow", "Semple Stadium", "Barry Teehan"], ["2006", "Dublin", "0-16 (16)", "1-6 (9)", "Kerry", "Semple Stadium", "Philip Brennan"], ["2007", "Laois", "2-19 (25)", "0-8 (8)", "Wicklow", "Semple Stadium", "Joe FitzPatrick"], ["2008", "Westmeath", "2-12 (18)", "0-12 (12)", "Carlow", "Gaelic Grounds", "Brendan Murtagh"], ["2009", "Offaly", "1-13 (16)", "0-13 (13)", "Wexford", "Semple Stadium", "Ger Oakley"], ["2010", "Wexford", "1-16 (19)", "2-9 (15)", "Clare", "Semple Stadium", "Diarmuid Lyng"], ["2011", "Limerick", "4-12 (24)", "2-13 (19)", "Clare", "Cusack Park", "Gavin O'Mahony"], ["2012", "Clare", "0-21 (21)", "1-16 (19)", "Limerick", "Gaelic Grounds", "Patrick Donnellan"], ["2013", "Dublin", "1-16 (19)", "1-15 (18)", "Limerick", "Semple Stadium", "TBC"]]}, "native_split": "test", "question": "Which team won the tournament in 2005, and what was the the runner-up team that year?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Offaly, Carlow", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "17df1b57444c60eb8a376c944f8078d4"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:3fe39c5bce73bdbc9e6340b961bbefe7", "source": "tablebench", "table_key": "a1b3ba1ff339d3249fdd1e4cdefe66784379eadf", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["rank", "nation", "gold", "silver", "bronze", "total"], ["1", "germany", "45", "33", "28", "106"], ["2", "switzerland", "39", "35", "32", "106"], ["3", "italy", "18", "18", "6", "42"], ["4", "united states", "12", "20", "31", "63"], ["5", "west germany", "11", "13", "12", "36"], ["6", "canada", "11", "11", "12", "34"], ["7", "east germany", "8", "9", "8", "25"], ["8", "great britain", "7", "6", "4", "17"], ["9", "austria", "6", "11", "14", "31"], ["10", "russia", "2", "5", "4", "11"], ["11", "romania", "2", "2", "2", "6"], ["12", "latvia", "2", "1", "1", "4"], ["13", "belgium", "1", "1", "1", "3"], ["14", "france", "1", "0", "4", "5"], ["15", "new zealand", "1", "0", "1", "2"], ["16", "czechoslovakia", "0", "2", "0", "2"], ["17", "sweden", "0", "0", "2", "2"], ["17", "soviet union", "0", "0", "2", "2"], ["19", "spain", "0", "0", "1", "1"]]}, "native_split": "test", "question": "Which nation ranked 4th in terms of gold medals won?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "united states", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3fe39c5bce73bdbc9e6340b961bbefe7"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:56af57cfe3097874ce49535911e9f039", "source": "tablebench", "table_key": "a1b3ba1ff339d3249fdd1e4cdefe66784379eadf", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["rank", "nation", "gold", "silver", "bronze", "total"], ["1", "germany", "45", "33", "28", "106"], ["2", "switzerland", "39", "35", "32", "106"], ["3", "italy", "18", "18", "6", "42"], ["4", "united states", "12", "20", "31", "63"], ["5", "west germany", "11", "13", "12", "36"], ["6", "canada", "11", "11", "12", "34"], ["7", "east germany", "8", "9", "8", "25"], ["8", "great britain", "7", "6", "4", "17"], ["9", "austria", "6", "11", "14", "31"], ["10", "russia", "2", "5", "4", "11"], ["11", "romania", "2", "2", "2", "6"], ["12", "latvia", "2", "1", "1", "4"], ["13", "belgium", "1", "1", "1", "3"], ["14", "france", "1", "0", "4", "5"], ["15", "new zealand", "1", "0", "1", "2"], ["16", "czechoslovakia", "0", "2", "0", "2"], ["17", "sweden", "0", "0", "2", "2"], ["17", "soviet union", "0", "0", "2", "2"], ["19", "spain", "0", "0", "1", "1"]]}, "native_split": "test", "question": "Which nation ranked 4th in the medal count ?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "united states", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "56af57cfe3097874ce49535911e9f039"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:daa1e132c435be7e111cbe2363bb2c23", "source": "tablebench", "table_key": "5b131e7a8889a6eaa90ae4954588367927513d57", "grid": {"TD": 1, "LD": 0, "nrows": 16, "ncols": 6, "section_rows": [], "cells": [["rank", "nation", "gold", "silver", "bronze", "total"], ["1", "canada", "1", "1", "2", "4"], ["2", "lebanon", "1", "1", "0", "2"], ["3", "bulgaria", "1", "0", "1", "2"], ["4", "benin", "1", "0", "0", "1"], ["4", "quebec", "1", "0", "0", "1"], ["4", "cape verde", "1", "0", "0", "1"], ["4", "ivory coast", "1", "0", "0", "1"], ["8", "vietnam", "0", "2", "0", "2"], ["9", "central african republic", "0", "1", "0", "1"], ["9", "french community of belgium", "0", "1", "0", "1"], ["9", "niger", "0", "1", "0", "1"], ["9", "democratic republic of the congo", "0", "1", "0", "1"], ["9", "republic of the congo", "0", "1", "0", "1"], ["14", "cameroon", "0", "0", "1", "1"], ["14", "madagascar", "0", "0", "1", "1"]]}, "native_split": "test", "question": "According to the table, which nation won 1 gold medal and no silver or bronze medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "benin, quebec, cape verde, ivory coast", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "daa1e132c435be7e111cbe2363bb2c23"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:afe452325ae2a67d28cd447dd37686d6", "source": "tablebench", "table_key": "250e3ee746f2a79be98501317f66a7d1d81058e0", "grid": {"TD": 1, "LD": 0, "nrows": 17, "ncols": 7, "section_rows": [], "cells": [["year", "winner", "jockey", "trainer", "owner", "distance (miles)", "time"], ["2013", "war dancer", "alan garcia", "kenneth g mcpeek", "magdalena racing", "1 - 1 / 4", "2:03.57"], ["2012", "silver max", "robby albarado", "dale l romans", "bacon / wells", "1 - 1 / 4", "2:04.05"], ["2011", "air support", "alex solis", "shug mcgaughey", "stuart janney iii", "1 - 1 / 4", "2:00.80"], ["2010", "paddy o'prado", "kent j desormeaux", "dale l romans", "winchell thoroughbreds", "1 - 1 / 4", "2:02.58"], ["2009", "battle of hastings", "tyler baze", "jeff mullins", "michael house", "1 - 1 / 4", "2:03.29"], ["2008", "gio ponti", "garrett gomez", "christophe clement", "castleton lyons", "1 - 1 / 4", "2:02.22"], ["2007", "red giant", "horacio karamanos", "todd a pletcher", "peachtree stable", "1 - 1 / 4", "1:59.62"], ["2006", "go between", "garrett k gomez", "william i mott", "peter vegso", "1 - 1 / 4", "1:59.74"], ["2005", "english channel", "john r velazquez", "todd a pletcher", "james t scatuorchio", "1 - 1 / 4", "2:02.57"], ["2004", "kitten 's joy", "edgar s prado", "dale l romans", "ken and sarah ramsey", "1 - 1 / 4", "2:01.22"], ["2003", "silver tree", "edgar s prado", "william i mott", "peter vegso", "1 - 1 / 4", "2:01.11"], ["2002", "orchard park", "edgar s prado", "william i mott", "peter vegso", "1 - 1 / 4", "2:03.10"], ["2001", "potaro", "brent bartram", "jonathan sheppard", "augustin stable", "1 - 1 / 4", "2:02.18"], ["2000", "lightning paces", "greg hutton", "john j robb", "tulip hill farm", "1 - 1 / 4", "2:02.18"], ["1999", "phi beta doc", "ramon dominguez", "robert w leonard", "dennis foster / r leonard", "1 - 1 / 4", "1:59.97"], ["1998", "crowd pleaser", "jean - luc samyn", "jonathan sheppard", "augustin stable", "1 - 1 / 4", "2:00.28"]]}, "native_split": "test", "question": "Which trainer has trained the most winners with a winning time of less than 2:02 minutes?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "william i mott", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "afe452325ae2a67d28cd447dd37686d6"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:1c7c310b185c9507faa6e8b0f38a1213", "source": "tablebench", "table_key": "dc8caad210da90268900fa3d92cbbaefaebe90f0", "grid": {"TD": 1, "LD": 0, "nrows": 21, "ncols": 10, "section_rows": [], "cells": [["Position", "Club", "Played", "Points", "Wins", "Draws", "Losses", "Goals for", "Goals against", "Goal Difference"], ["1", "UE Lleida", "38", "57+19", "23", "11", "4", "56", "20", "36"], ["2", "Real Valladolid", "38", "52+14", "20", "12", "6", "50", "30", "20"], ["3", "Racing de Santander", "38", "52+14", "23", "6", "9", "56", "38", "18"], ["4", "RCD Mallorca", "38", "50+12", "21", "8", "9", "57", "34", "23"], ["5", "Real Betis", "38", "43+5", "16", "11", "11", "49", "33", "16"], ["6", "Real Madrid B", "38", "42+4", "15", "12", "11", "57", "41", "16"], ["7", "Atlético Marbella", "38", "42+4", "17", "8", "13", "45", "41", "4"], ["8", "Barcelona B", "38", "39+1", "15", "9", "14", "59", "55", "4"], ["9", "CP Mérida", "38", "39+1", "13", "13", "12", "43", "42", "1"], ["10", "CD Castellón", "38", "36-2", "13", "10", "15", "40", "45", "-5"], ["11", "CD Badajoz", "38", "36-2", "14", "8", "16", "37", "36", "1"], ["12", "SD Compostela", "38", "35-3", "10", "15", "13", "35", "39", "-4"], ["13", "Villarreal CF", "38", "34-4", "13", "8", "17", "38", "51", "-14"], ["14", "Palamós CF", "38", "33-5", "12", "9", "17", "33", "50", "-17"], ["15", "Athletic de Bilbao B", "38", "33-5", "9", "15", "14", "33", "34", "-1"], ["16", "SD Eibar", "38", "32-6", "10", "12", "16", "33", "44", "-11"], ["17", "UE Figueres", "38", "32-6", "11", "10", "17", "41", "59", "-18"], ["18", "CD Lugo", "38", "25-13", "7", "11", "20", "23", "41", "-18"], ["19", "Sestao", "38", "24-14", "7", "10", "21", "29", "54", "-25"], ["20", "CE Sabadell FC 1", "38", "24-14", "8", "8", "22", "30", "57", "-27"]]}, "native_split": "test", "question": "Which team finished in a goal difference of 16?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Real Betis, Real Madrid B", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "1c7c310b185c9507faa6e8b0f38a1213"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:676e45fc03dc0b9f312ff42d35bda0d1", "source": "tablebench", "table_key": "5b5ad70e06cc7dcac00552f98f17d2d350f8ceb7", "grid": {"TD": 1, "LD": 0, "nrows": 23, "ncols": 6, "section_rows": [], "cells": [["Year", "Competition", "Venue", "Position", "Event", "Notes"], ["Representing Great Britain", "Representing Great Britain", "Representing Great Britain", "Representing Great Britain", "Representing Great Britain", "Representing Great Britain"], ["2009", "World Youth Championships", "Brixen, Italy", "1st", "100 m", "11.39"], ["2009", "World Youth Championships", "Brixen, Italy", "1st", "200 m", "23.08"], ["2010", "World Junior Championships", "Moncton, New Brunswick, Canada", "1st", "100m", "11.40 (wind: -0.7 m/s)"], ["2010", "World Junior Championships", "Moncton, New Brunswick, Canada", "2nd", "200m", "23.19 (wind: -0.5 m/s)"], ["2010", "World Junior Championships", "Moncton, New Brunswick, Canada", "—", "4 × 100 m relay", "DNF"], ["2011", "European Indoor Championships", "Paris, France", "4th", "60 m", "7.21"], ["2011", "European Junior Championships", "Tallinn, Estonia", "1st", "100 m", "11.18"], ["2011", "European Junior Championships", "Tallinn, Estonia", "1st", "200 m", "22.94"], ["2011", "European Junior Championships", "Tallinn, Estonia", "3rd", "4 × 100 m", "45.00"], ["2012", "World Indoor Championships", "Istanbul, Turkey", "16th (sf)", "60 m", "7.32"], ["2013", "European U23 Championships", "Tampere, Finland", "2nd", "100 m", "11.42 (wind: -0.7 m/s)"], ["2013", "European U23 Championships", "Tampere, Finland", "1st", "200 m", "22.92 (wind: -0.5 m/s)"], ["2013", "European U23 Championships", "Tampere, Finland", "2nd", "4 × 100 m", "43.83"], ["2013", "World Championships", "Moscow, Russia", "semi-final", "200 m", "23.21"], ["2014", "Commonwealth Games", "Glasgow, Scotland", "2nd", "200 m", "22.50"], ["2014", "Commonwealth Games", "Glasgow, Scotland", "3rd", "4 × 100 m relay", "43.10"], ["2014", "European Championships", "Zurich, Switzerland", "2nd", "200 m", "22.46"], ["2014", "European Championships", "Zurich, Switzerland", "1st", "4 × 100 m relay", "42.25 NR"], ["2015", "World Championships", "Beijing, China", "4th", "4 × 100 m relay", "42.10"], ["2016", "Olympic Games", "Rio de Janeiro, Brazil", "22nd (sf)", "200 m", "22.99"], ["2018", "European Championships", "Berlin, Germany", "13th (sf)", "200 m", "23.28"]]}, "native_split": "test", "question": "In which year did the athlete win the gold medal in the 200m event at the European Junior Championships?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2011", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "676e45fc03dc0b9f312ff42d35bda0d1"}, "dataset": "tablebench", "subtype": "FactChecking"}
{"id": "tablebench:dp-test:5c61003ff264d8ec6019f2440dce475e", "source": "tablebench", "table_key": "23001cce7e0bbc40aa039c8917528793dda3891a", "grid": {"TD": 1, "LD": 0, "nrows": 39, "ncols": 5, "section_rows": [], "cells": [["name", "latitude", "longitude", "diameter (km)", "named after"], ["caccini", "17.4", "170.4", "38.1", "francesca caccini , italian composer"], ["caitlin", "- 65.3", "12.0", "14.7", "irish first name"], ["caiwenji", "- 12.4", "287.6", "22.6", "cai wenji , chinese poet"], ["caldwell", "23.6", "112.4", "51.0", "taylor caldwell , american author"], ["callas", "2.4", "27.0", "33.8", "maria callas , american singer"], ["callirhoe", "21.2", "140.7", "33.8", "callirhoe , greek sculptor"], ["caroline", "6.9", "306.3", "18.0", "french first name"], ["carr", "- 24", "295.7", "31.9", "emily carr , canadian artist"], ["carreno", "- 3.9", "16.1", "57.0", "teresa carreño , n venezuela pianist"], ["carson", "- 24.2", "344.1", "38.8", "rachel carson , american biologist"], ["carter", "5.3", "67.3", "17.5", "maybelle carter , american singer"], ["castro", "3.4", "233.9", "22.9", "rosalía de castro , galician poet"], ["cather", "47.1", "107.0", "24.6", "willa cather , american novelist"], ["centlivre", "19.1", "290.4", "28.8", "susanna centlivre , english actress"], ["chapelle", "6.4", "103.8", "22.0", "georgette chapelle , american journalist"], ["chechek", "- 2.6", "272.3", "7.2", "tuvan first name"], ["chiyojo", "- 47.8", "95.7", "40.2", "chiyojo , japanese poet"], ["chloe", "- 7.4", "98.6", "18.6", "greek first name"], ["cholpon", "40", "290.0", "6.3", "kyrgyz first name"], ["christie", "28.3", "72.7", "23.3", "agatha christie , english author"], ["chubado", "45.3", "5.6", "7.0", "fulbe first name"], ["clara", "- 37.5", "235.3", "3.2", "latin first name"], ["clementina", "35.9", "208.6", "4.0", "portuguese form of clementine , french first name"], ["cleopatra", "65.8", "7.1", "105.0", "cleopatra , egyptian queen"], ["cline", "- 21.8", "317.1", "38.0", "patsy cline , american singer"], ["clio", "6.3", "333.5", "11.4", "greek first name"], ["cochran", "51.9", "143.4", "100.0", "jacqueline cochran , american aviator"], ["cohn", "- 33.3", "208.1", "18.3", "carola cohn , australian artist"], ["colleen", "- 60.8", "162.2", "13.5", "irish first name"], ["comnena", "1.2", "343.7", "19.5", "anna comnena , byzantine princess and writer"], ["conway", "48.3", "39.0", "49.3", "lady anne finch conway , english natural scientist"], ["cori", "25.4", "72.9", "56.1", "gerty cori , czech biochemist"], ["corinna", "22.9", "40.6", "19.2", "corinna , greek poet"], ["corpman", "0.3", "151.8", "46.0", "elizabeth koopman hevelius , astronomer"], ["cortese", "- 11.4", "218.4", "27.7", "isabella cortese , italian physician"], ["cotton", "70.8", "300.2", "48.1", "eugénie cotton , french physicist"], ["cunitz", "14.5", "350.9", "48.6", "maria cunitz , silesian astronomer"], ["cynthia", "- 16.7", "347.5", "15.9", "greek first name"]]}, "native_split": "test", "question": "What is the total diameter of all craters with a diameter greater than 40 km?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "601.3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "5c61003ff264d8ec6019f2440dce475e"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:22ca06f97417eaae94a58549e8bfd1d4", "source": "tablebench", "table_key": "d1b51cc1e879dd336091f5033d916d6156b316bf", "grid": {"TD": 1, "LD": 0, "nrows": 27, "ncols": 6, "section_rows": [], "cells": [["driver", "points", "races", "wins", "second", "third"], ["gunther gooverts", "182", "27", "2", "-", "3"], ["gunther gooverts", "135", "18", "1", "2", "1"], ["gunther gooverts", "27", "8", "-", "-", "1"], ["gunther gooverts", "155", "16", "-", "3", "2"], ["gert devoldere", "3", "2", "-", "-", "-"], ["daniël willemsen", "40", "2", "2", "-", "-"], ["martin gölz", "90", "20", "-", "-", "-"], ["gerton kops", "17", "2", "-", "-", "-"], ["peter steegmans", "16", "2", "-", "-", "-"], ["daniël willemsen", "320", "22", "5", "4", "7"], ["daniël willemsen", "377", "22", "8", "5", "3"], ["are kaurit", "268", "16", "-", "3", "2"], ["daniël willemsen", "88", "4", "-", "4", "-"], ["kristers serģis", "501", "26", "12", "7", "1"], ["kristers serģis", "246", "12", "6", "1", "2"], ["frank hofman", "22", "2", "-", "-", "-"], ["daniël willemsen", "478", "22", "15", "4", "-"], ["daniël willemsen", "341", "16", "13", "-", "-"], ["nicky pulinx", "22", "4", "-", "-", "-"], ["jarno van den boomen", "8", "2", "-", "-", "-"], ["gerrit van werven", "6", "2", "-", "-", "-"], ["daniël willemsen", "341", "17", "11", "-", "-"], ["peter steegmans", "212", "20", "-", "1", "1"], ["daniël willemsen", "437", "21", "14", "3", "-"], ["ben adriaenssen", "385", "22", "-", "-", "6"], ["overall 1993 - 2012", "4717", "327", "89", "37", "29"]]}, "native_split": "test", "question": "In the context of racing, a \"podium finish\" is defined as a driver finishing in the top three positions (first, second, or third) in a race. Based on the provided data, which driver has achieved the most podium finishes in their career?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "daniël willemsen", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "22ca06f97417eaae94a58549e8bfd1d4"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:0a593de4b82e18d255d786e3fee0d85f", "source": "tablebench", "table_key": "eb7904b46a8ec7ee1d5e9edc6caeb2588992082b", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 8, "section_rows": [], "cells": [["no in series", "no in season", "title", "directed by", "written by", "original air date", "production code", "us viewers (millions)"], ["67", "1", "charmed again (part 1)", "michael schultz", "brad kern", "october 4 , 2001", "4301801", "6.0"], ["68", "2", "charmed again (part 2)", "michael schultz", "brad kern", "october 4 , 2001", "4301801", "6.0"], ["69", "3", "hell hath no fury", "chris long", "krista vernoff", "october 11 , 2001", "4301069", "5.0"], ["70", "4", "enter the demon", "joel j feigenbaum", "daniel cerone", "october 18 , 2001", "4301071", "5.7"], ["71", "5", "size matters", "noel nosseck", "nell scovell", "october 25 , 2001", "4301070", "5.3"], ["72", "6", "a knight to remember", "david straiton", "alison schapker & monica breen", "november 1 , 2001", "4301072", "4.7"], ["73", "7", "brain drain", "john behring", "curtis kheel", "november 8 , 2001", "4301073", "4.7"], ["74", "8", "black as cole", "les landau", "abbey campbell , brad kern & nell scovell", "november 15 , 2001", "4301074", "5.1"], ["75", "9", "muse to my ears", "joel j feigenbaum", "krista vernoff", "december 13 , 2001", "4301075", "4.5"], ["76", "10", "a paige from the past", "james l conway", "daniel cerone", "january 17 , 2002", "4301076", "3.4"], ["77", "11", "trial by magic", "chip scott laughlin", "michael gleason", "january 24 , 2002", "4301077", "4.1"], ["78", "12", "lost and bound", "noel nosseck", "nell scovell", "january 31 , 2002", "4301078", "3.9"], ["79", "13", "charmed and dangerous", "jon pare", "alison schapker & monica breen", "february 7 , 2002", "4301079", "4.7"], ["80", "14", "the three faces of phoebe", "joel j feigenbaum", "curtis kheel", "february 14 , 2002", "4301080", "4.7"], ["81", "15", "marry - go - round", "chris long", "daniel cerone", "march 14 , 2002", "4301081", "4.5"], ["82", "16", "the fifth halliwheel", "david straiton", "krista vernoff", "march 21 , 2002", "4301082", "4.8"], ["83", "17", "saving private leo", "john behring", "daniel cerone & doug e jones", "march 28 , 2002", "4301083", "3.9"], ["84", "18", "bite me", "john t kretchmer", "curtis kheel", "april 18 , 2002", "4301084", "3.6"], ["85", "19", "we 're off to see the wizard", "timothy lonsdale", "alison schapker & monica breen", "april 25 , 2002", "4301085", "4.2"], ["86", "20", "long live the queen", "jon parã", "krista vernoff", "may 2 , 2002", "4301086", "2.8"], ["87", "21", "womb raider", "mel damski", "daniel cerone", "may 9 , 2002", "4301087", "5.0"]]}, "native_split": "test", "question": "Which episode had the lowest number of US viewers (in millions)?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "long live the queen", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0a593de4b82e18d255d786e3fee0d85f"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:e15204a55dd9aa141e95354c91a62bd0", "source": "tablebench", "table_key": "effd298cc6683084d45e801abce89e5689e63ee7", "grid": {"TD": 1, "LD": 0, "nrows": 18, "ncols": 7, "section_rows": [], "cells": [["interval name", "size (steps)", "size (cents)", "just ratio", "just (cents)", "error", "audio"], ["perfect fifth", "9", "720", "3:2", "701.96", "+ 18.04", "play category : articles with haudio microformats"], ["septimal tritone", "7", "560", "7:5", "582.51", "22.51", "play category : articles with haudio microformats"], ["11:8 wide fourth", "7", "560", "11:8", "551.32", "+ 8.68", "play category : articles with haudio microformats"], ["15:11 wide fourth", "7", "560", "15:11", "536.95", "+ 23.05", "play category : articles with haudio microformats"], ["perfect fourth", "6", "480", "4:3", "498.04", "18.04", "play category : articles with haudio microformats"], ["septimal major third", "5", "400", "9:7", "435.08", "35.08", "play category : articles with haudio microformats"], ["undecimal major third", "5", "400", "14:11", "417.51", "17.51", "play category : articles with haudio microformats"], ["major third", "5", "400", "5:4", "386.31", "+ 13.69", "play category : articles with haudio microformats"], ["minor third", "4", "320", "6:5", "315.64", "+ 4.36", "play category : articles with haudio microformats"], ["septimal minor third", "3", "240", "7:6", "266.87", "26.87", "play category : articles with haudio microformats"], ["septimal whole tone", "3", "240", "8:7", "231.17", "+ 8.83", "play category : articles with haudio microformats"], ["major tone", "3", "240", "9:8", "203.91", "+ 36.09", "play category : articles with haudio microformats"], ["minor tone", "2", "160", "10:9", "182.4", "22.40", "play category : articles with haudio microformats"], ["greater undecimal neutral second", "2", "160", "11:10", "165.0", "5.00", "play category : articles with haudio microformats"], ["lesser undecimal neutral second", "2", "160", "12:11", "150.63", "+ 9.36", "play category : articles with haudio microformats"], ["just diatonic semitone", "1", "80", "16:15", "111.73", "31.73", "play category : articles with haudio microformats"], ["septimal chromatic semitone", "1", "80", "21:20", "84.46", "4.47", "play category : articles with haudio microformats"]]}, "native_split": "test", "question": "What is the total error (in cents) of the intervals with the smallest sizes (in steps)?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "36.2", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "e15204a55dd9aa141e95354c91a62bd0"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:d5f9a7bbcbc5f26735c8f332d75a2c36", "source": "tablebench", "table_key": "3511d9cc8c142f74746e7168a2c9195103a0b4a0", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 7, "section_rows": [], "cells": [["region", "land area (km 2 )", "rainfall by depth (mm / year)", "rainfall by volume (km 3 / year)", "surface run off (km 3 / year)", "infiltration (km 3 / year)", "evapotranspiration (km 3 / year)"], ["chorotega", "9552.4", "2006", "19.2", "5.7", "3.5", "10.3"], ["huetar norte", "9001.5", "3527", "31.8", "14.9", "9.6", "7.5"], ["huetar atlántico", "9688.5", "3933", "38.1", "17.6", "9.3", "11.1"], ["pacífico central", "4722.9", "2801", "13.2", "5.2", "2.2", "4.9"], ["central", "8543.2", "3461", "29.6", "13.0", "7.0", "8.6"], ["brunca", "9294.5", "3809", "35.4", "18.6", "5.6", "12.2"]]}, "native_split": "test", "question": "Which region has the highest rainfall by volume (km³/year)?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "huetar atlántico", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "d5f9a7bbcbc5f26735c8f332d75a2c36"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:aa9a653502bd85c8923c87279499a902", "source": "tablebench", "table_key": "c9416c81e5a948de3e8062a439e8b533a3f14406", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 9, "section_rows": [], "cells": [["rank", "airport", "total passengers", "% change 2006 / 2007", "international passengers", "domestic passengers", "transit passengers", "aircraft movements", "freight (metric tonnes)"], ["1", "london heathrow", "68066028", "0.8%", "62098911", "5753476", "213641", "481476", "1310987"], ["2", "london gatwick", "35216113", "3.1%", "31142002", "4023402", "50709", "266550", "171078"], ["3", "london stansted", "23779697", "0.4%", "21204946", "2554304", "20447", "208462", "203747"], ["4", "manchester", "22112625", "1.5%", "18662468", "3229255", "220902", "222703", "165366"], ["5", "london luton", "9927321", "5.3%", "8427894", "1491467", "7960", "120238", "38095"], ["6", "birmingham airport", "9226340", "0.9%", "7592240", "1541815", "92285", "114679", "13585"], ["7", "edinburgh", "9047558", "5.1%", "3417891", "5619309", "10358", "128172", "19292"], ["8", "glasgow international", "8795727", "0.6%", "4131512", "4594575", "69640", "108305", "4276"], ["9", "bristol", "5926774", "2.9%", "4608290", "1275566", "42918", "76428", "20"], ["10", "newcastle", "5650716", "4.0%", "3948594", "1675013", "27109", "79200", "785"], ["11", "liverpool", "5468510", "10.2%", "4636149", "827085", "5276", "86668", "3709"], ["12", "east midlands", "5413360", "14.5%", "4709855", "696649", "6856", "93989", "274753"], ["13", "belfast international", "5272664", "4.6%", "1788807", "3447248", "36609", "77395", "38429"], ["14", "aberdeen", "3412257", "7.8%", "1475988", "1935152", "1117", "121927", "3434"], ["15", "london city", "2912123", "23.5%", "2214884", "697239", "0", "91177", "0"], ["16", "leeds bradford", "2881539", "3.2%", "2229283", "630575", "21681", "65249", "109"], ["17", "glasgow prestwick", "2422332", "1.0%", "1827592", "593117", "1623", "47910", "31517"], ["18", "belfast city", "2186993", "3.9%", "93547", "2093320", "126", "43022", "1057"], ["19", "cardiff", "2111148", "4.3%", "1665247", "428260", "17641", "43963", "2391"]]}, "native_split": "test", "question": "If the total passengers at glasgow international Airport increase by 15% in 2008, approximately how many passengers would the airport handle in 2008?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "10115086", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "aa9a653502bd85c8923c87279499a902"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:73a54b17717bad013f15c02d5d9b870e", "source": "tablebench", "table_key": "c6c81d2ed9e6691c84894298e339abd7b160b486", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 5, "section_rows": [], "cells": [["year", "number of examinees", "number of passed students", "pass percentage", "obtained gpa - 5"], ["2005", "314", "239", "67.75%", "31"], ["2006", "331", "278", "72.37%", "54"], ["2007", "336", "260", "68.62%", "63"], ["2008", "346", "274", "75.54%", "79"], ["2009", "360", "297", "78.35%", "83"], ["2010", "364", "322", "79.68%", "85"]]}, "native_split": "test", "question": "What is the percentage increase in the number of passed students from 2005 to 2010?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "34.73%", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "73a54b17717bad013f15c02d5d9b870e"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:63359e8db2964276d15c92a05ec20ffb", "source": "tablebench", "table_key": "ff38a284f881839688c29ac79374b58e24c14b89", "grid": {"TD": 1, "LD": 0, "nrows": 25, "ncols": 8, "section_rows": [], "cells": [["rank", "rank fortune 500", "name", "headquarters", "revenue (millions)", "profit (millions)", "employees", "industry"], ["1", "17", "sinopec", "beijing", "131636.0", "3703.1", "681900", "oil"], ["2", "24", "china national petroleum", "beijing", "110520.2", "13265.3", "1086966", "oil"], ["3", "29", "state grid corporation", "beijing", "107185.5", "2237.7", "1504000", "utilities"], ["4", "170", "industrial and commercial bank of china", "beijing", "36832.9", "6179.2", "351448", "banking"], ["5", "180", "china mobile limited", "beijing", "35913.7", "6259.7", "130637", "telecommunications"], ["6", "192", "china life insurance", "beijing", "33711.5", "173.9", "77660", "insurance"], ["7", "215", "bank of china", "beijing", "30750.8", "5372.3", "232632", "banking"], ["8", "230", "china construction bank", "beijing", "28532.3", "5810.3", "297506", "banking"], ["9", "237", "china southern power grid", "guangzhou", "27966.1", "1074.1", "178053", "utilities"], ["10", "275", "china telecom", "beijing", "24791.3", "2279.7", "400299", "telecommunications"], ["11", "277", "agricultural bank of china", "beijing", "24475.5", "728.4", "452464", "banking"], ["12", "290", "hutchison whampoa", "hong kong", "23661.0", "2578.3", "220000", "various sectors"], ["13", "299", "sinochem corporation", "beijing", "23109.2", "344.7", "20343", "various sectors"], ["14", "307", "baosteel", "shanghai", "22663.4", "1622.2", "91308", "steel"], ["15", "342", "china railway engineering", "beijing", "20520.4", "142.6", "275866", "railway"], ["16", "384", "china railway construction", "beijing", "18735.7", "70.2", "245540", "railway"], ["17", "385", "first automotive works", "changchun", "18710.7", "70.0", "136010", "automobile"], ["18", "396", "china state construction", "beijing", "18163.2", "281.3", "294309", "construction"], ["19", "402", "saic motor", "shanghai", "18010.1", "89.7", "72416", "automobile"], ["20", "405", "cofco limited", "beijing", "17953.2", "281.0", "82481", "various sectors"], ["21", "435", "china minmetals", "beijing", "16902.2", "154.4", "32594", "metal trading"], ["22", "457", "jardine matheson", "hong kong / hamilton", "16281.0", "1348.0", "240000", "various sectors"], ["23", "469", "china national offshore oil", "beijing", "16038.9", "3007.1", "44000", "oil"], ["24", "488", "china ocean shipping", "beijing", "15413.5", "1092.9", "79616", "shipping"]]}, "native_split": "test", "question": "How many companies in the banking industry are listed in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "4", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "63359e8db2964276d15c92a05ec20ffb"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:c56b85419c6abec8753dcc96be88024f", "source": "tablebench", "table_key": "445f9c848c361a04e5b4c24f3d669674a563dfbb", "grid": {"TD": 1, "LD": 0, "nrows": 26, "ncols": 5, "section_rows": [], "cells": [["ensemble", "gold medals", "silver medals", "bronze medals", "total medals"], ["amador valley hs", "0", "1", "0", "1"], ["ayala high school", "4", "2", "1", "7"], ["baldwinsville hs", "2", "0", "0", "2"], ["claremont hs", "1", "1", "0", "2"], ["downers grove hs", "0", "0", "1", "1"], ["father ryan hs", "0", "1", "0", "1"], ["fort mill hs", "2", "1", "2", "5"], ["franklin central hs", "6", "0", "0", "6"], ["gateway high school", "2", "1", "1", "4"], ["goshen hs", "0", "2", "1", "3"], ["harrison central paragon hs", "0", "0", "1", "1"], ["james logan high school", "1", "1", "0", "2"], ["john overton hs", "0", "1", "2", "3"], ["king philip high school", "0", "1", "0", "1"], ["mansfield hs", "0", "1", "0", "1"], ["mission viejo high school", "0", "1", "0", "1"], ["muscle shoals hs", "1", "1", "2", "4"], ["new philadelphia hs", "0", "1", "0", "1"], ["northglenn hs", "0", "0", "1", "1"], ["rangeview hs", "0", "1", "0", "1"], ["roland hayes school", "0", "0", "1", "1"], ["tarpon springs hs", "0", "1", "0", "1"], ["tunstall hs", "0", "3", "4", "7"], ["warsaw community hs", "0", "0", "1", "1"], ["woodbridge hs", "1", "0", "0", "1"]]}, "native_split": "test", "question": "Medal Ratio is calculated as the number of gold medals divided by the total number of medals. Based on this definition, which high school has the highest medal ratio?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "baldwinsville hs, franklin central hs, woodbridge hs", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c56b85419c6abec8753dcc96be88024f"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:9eb17e466a9ae8c241c276e69e368046", "source": "tablebench", "table_key": "688de8d396dcb340100552d70c02ed48cb09aeae", "grid": {"TD": 1, "LD": 0, "nrows": 26, "ncols": 7, "section_rows": [], "cells": [["Year", "Dates", "No. of shows", "No. of performances", "Attendance", "Venues", "References"], ["1994", "June 23 – July 2", "53", "315", "4,630", "6", "-"], ["1995", "June 22 – July 3", "≤50", "400", "5,500", "5", "-"], ["1996", "June 20–30", "46", "250", "4,464", "5", "-"], ["1997", "June 19–29", "35", "—", "4,300", "5", "-"], ["1998", "July 30 – August 9", "38", "~200", "6,573", "6", "-"], ["1999", "July 29 – August 8", "68", "—", "15,447", "10", "-"], ["2000", "July 28 – August 6", "100", "500", ">23,000", "19", "-"], ["2001", "August 3–12", "120", "—", "28,835", "21", "-"], ["2002", "August 2–11", "148", ">675", "32,000", "14", "-"], ["2003", "August 1–10", "162", "783", "40,500", "20", "-"], ["2004", "August 6–15", "176", "900", "43,836", "24", "-"], ["2005", "August 4–14", "168", "855", "44,630", "20", "-"], ["2006", "August 3–13", "165", "890", "44,814", "23", "-"], ["2007", "August 2–12", "162", "872", "37,752", "23", "-"], ["2008", "July 31 – August 10", "156", "808", "40,926", "18", "-"], ["2009", "July 30 – August 9", "162", "843", "46,189", "22", "-"], ["2010", "August 5–15", "169", "876", "50,256", "15", "-"], ["2011", "August 4–14", "168", "865", "48,350", "18", "-"], ["2012", "August 2–12", "164", "840", "48,432", "15", "-"], ["2013", "August 1–11", "177", "897", "50,007", "16", "-"], ["2014", "July 31 – August 10", "169", "878", "50,265", "15", "-"], ["2015", "July 30 – August 9", "174", "909", "50,338", "24", "-"], ["2016", "August 4–14", "168", "869", "47,882", "19", "-"], ["2017", "August 3–13", "167", "850", "46,076", "17", "-"], ["2018", "August 2–12", "138", "694", "~36,400", "16", "-"]]}, "native_split": "test", "question": "What is the average number of performances per year between 2004 and 2013?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "864.6", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9eb17e466a9ae8c241c276e69e368046"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:03ef349b3920a798e7c9e3b44589d702", "source": "tablebench", "table_key": "df700612bf91aa29e8909a87bf7a437186fdc722", "grid": {"TD": 1, "LD": 0, "nrows": 33, "ncols": 5, "section_rows": [], "cells": [["team", "wins", "losses", "ties", "win pct"], ["arizona cardinals", "2", "1", "0", "0.667"], ["atlanta falcons", "3", "1", "1", "0.7"], ["baltimore ravens", "13", "9", "0", "0.591"], ["buffalo bills", "5", "2", "0", "0.714"], ["carolina panthers", "3", "1", "0", "0.75"], ["chicago bears", "3", "1", "0", "0.75"], ["cincinnati bengals", "21", "9", "0", "0.7"], ["cleveland browns", "19", "5", "0", "0.792"], ["dallas cowboys", "1", "2", "0", "0.333"], ["denver broncos", "1", "3", "0", "0.25"], ["detroit lions", "4", "1", "0", "0.8"], ["green bay packers", "2", "2", "0", "0.5"], ["houston texans", "1", "1", "0", "0.5"], ["indianapolis colts", "4", "1", "0", "0.8"], ["jacksonville jaguars", "8", "10", "0", "0.444"], ["kansas city chiefs", "5", "3", "0", "0.625"], ["miami dolphins", "5", "2", "0", "0.714"], ["minnesota vikings", "2", "2", "0", "0.5"], ["new england patriots", "4", "3", "0", "0.571"], ["new orleans saints", "2", "1", "0", "0.667"], ["new york giants", "2", "1", "0", "0.667"], ["new york jets", "4", "1", "0", "0.8"], ["oakland raiders", "5", "2", "0", "0.714"], ["philadelphia eagles", "2", "2", "0", "0.5"], ["st louis rams", "1", "2", "0", "0.333"], ["san diego chargers", "7", "2", "0", "0.778"], ["san francisco 49ers", "1", "3", "0", "0.25"], ["seattle seahawks", "2", "4", "0", "0.333"], ["tampa bay buccaneers", "3", "1", "0", "0.75"], ["tennessee titans", "11", "12", "0", "0.478"], ["washington redskins", "3", "0", "0", "1.0"], ["totals :", "149", "90", "1", "0.623"]]}, "native_split": "test", "question": "How many teams have a win percentage of 0.7 or higher?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "14", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "03ef349b3920a798e7c9e3b44589d702"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:629103144efe26a2da56a953c77fa3f8", "source": "tablebench", "table_key": "377286a19113440f8ab150d576eb3bb007568968", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["Rank", "Nation", "Gold", "Silver", "Bronze", "Total"], ["1.0", "Great Britain (GBR)", "6", "3", "2", "11"], ["2.0", "South Africa (RSA)", "1", "2", "1", "4"], ["3.0", "Canada (CAN)", "1", "1", "0", "2"], ["4.0", "Australia (AUS)", "0", "1", "1", "2"], ["None", "Hong Kong (HKG)", "0", "1", "1", "2"], ["6.0", "Israel (ISR)", "0", "0", "2", "2"], ["7.0", "South Korea (KOR)", "0", "0", "1", "1"]]}, "native_split": "test", "question": "How many nations have won at least one gold medal?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "629103144efe26a2da56a953c77fa3f8"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:6f416284e0b161716e479a0f4b3e6772", "source": "tablebench", "table_key": "b83f28e3db5d2199522ed7ef0d3dc9f35d082433", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 6, "section_rows": [], "cells": [["year", "theme", "artist", "composition", "mintage", "issue price"], ["2000", "year of the dragon", "harvey chan", "75% gold , 25% silver", "8874", "388.88"], ["2001", "year of the snake", "harvey chan", "75% gold , 25% silver", "6571", "388.88"], ["2002", "year of the horse", "harvey chan", "75% gold , 25% silver", "6843", "388.88"], ["2003", "year of the goat", "harvey chan", "75% gold , 25% silver", "3927", "398.88"], ["2004", "year of the monkey", "harvey chan", "75% gold , 25% silver", "3318", "398.88"], ["2005", "year of the rooster", "harvey chan", "75% gold , 25% silver", "4888", "398.88"], ["2006", "year of the dog", "harvey chan", "75% gold , 25% silver", "4888", "448.88"], ["2007", "year of the pig", "harvey chan", "75% gold , 25% silver", "4888", "498.95"], ["2008", "year of the rat", "harvey chan", "75% gold , 25% silver", "4888", "508.95"], ["2009", "year of the ox", "harvey chan", "75% gold , 25% silver", "4888", "638.88"], ["2010", "year of the tiger", "harvey chan", "75% gold , 25% silver", "4888", "555.55"], ["2011", "year of the rabbit", "harvey chan", "75% gold , 25% silver", "4888", "638.88"]]}, "native_split": "test", "question": "How many coins were issued between 2000 and 2005?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "6", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "6f416284e0b161716e479a0f4b3e6772"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:1d88ca6fdff3b3e0089571e8c933e316", "source": "tablebench", "table_key": "73f8121544365f214902e40fa9493ee44460aa0e", "grid": {"TD": 1, "LD": 0, "nrows": 26, "ncols": 4, "section_rows": [], "cells": [["Color", "Pin (Tip)", "Pin (Ring)", "Color.1"], ["White/Blue", "26", "1", "Blue/White"], ["White/Orange", "27", "2", "Orange/White"], ["White/Green", "28", "3", "Green/White"], ["White/Brown", "29", "4", "Brown/White"], ["White/Slate", "30", "5", "Slate/White"], ["Red/Blue", "31", "6", "Blue/Red"], ["Red/Orange", "32", "7", "Orange/Red"], ["Red/Green", "33", "8", "Green/Red"], ["Red/Brown", "34", "9", "Brown/Red"], ["Red/Slate", "35", "10", "Slate/Red"], ["Black/Blue", "36", "11", "Blue/Black"], ["Black/Orange", "37", "12", "Orange/Black"], ["Black/Green", "38", "13", "Green/Black"], ["Black/Brown", "39", "14", "Brown/Black"], ["Black/Slate", "40", "15", "Slate/Black"], ["Yellow/Blue", "41", "16", "Blue/Yellow"], ["Yellow/Orange", "42", "17", "Orange/Yellow"], ["Yellow/Green", "43", "18", "Green/Yellow"], ["Yellow/Brown", "44", "19", "Brown/Yellow"], ["Yellow/Slate", "45", "20", "Slate/Yellow"], ["Violet/Blue", "46", "21", "Blue/Violet"], ["Violet/Orange", "47", "22", "Orange/Violet"], ["Violet/Green", "48", "23", "Green/Violet"], ["Violet/Brown", "49", "24", "Brown/Violet"], ["Violet/Slate", "50", "25", "Slate/Violet"]]}, "native_split": "test", "question": "Which color combination has a higher 'Pin (Tip)' value, White/Blue or Red/Blue?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Red/Blue", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "1d88ca6fdff3b3e0089571e8c933e316"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:ee32b677b3e51d25608fcdbef787f33b", "source": "tablebench", "table_key": "470c07324e74c7ef0ea53fabdb49e9e015843127", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 4, "section_rows": [], "cells": [["-", "Total", "Male", "Female"], ["Population", "7159", "3645", "3514"], ["Children aged below 6 years", "913", "479", "434"], ["Scheduled caste", "1782", "890", "892"], ["Scheduled tribe", "744", "383", "361"], ["Literates", "4323", "2642", "1681"], ["Workers (all)", "3612", "2007", "1605"], ["Main workers (total)", "2187", "1463", "724"], ["Main workers: Cultivators", "756", "500", "256"], ["Main workers: Agricultural labourers", "830", "443", "387"], ["Main workers: Household industry workers", "107", "86", "21"], ["Main workers: Other", "494", "434", "60"], ["Non-workers (total)", "3547", "1638", "1909"]]}, "native_split": "test", "question": "Which gender has a higher number of literates, Male or Female?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Male", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ee32b677b3e51d25608fcdbef787f33b"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:66ea61b936831554ebaa4423d2600550", "source": "tablebench", "table_key": "ef845b13b53465f53f63101823b25a4108342e24", "grid": {"TD": 1, "LD": 0, "nrows": 28, "ncols": 5, "section_rows": [], "cells": [["election", "dã¡il", "share of votes", "seats", "total seats"], ["1927 (jun)", "5th", "26.2%", "44", "153"], ["1927 (sep)", "6th", "35.2%", "57", "153"], ["1932", "7th", "44.5%", "72", "153"], ["1933", "8th", "49.7%", "76", "153"], ["1937", "9th", "45.2%", "68", "138"], ["1938", "10th", "51.9%", "76", "138"], ["1943", "11th", "41.8%", "66", "138"], ["1944", "12th", "48.9%", "75", "138"], ["1948", "13th", "41.9%", "67", "147"], ["1951", "14th", "46.3%", "68", "147"], ["1954", "15th", "43.4%", "65", "147"], ["1957", "16th", "48.3%", "78", "147"], ["1961", "17th", "43.8%", "70", "144"], ["1965", "18th", "47.7%", "72", "144"], ["1969", "19th", "44.6%", "74", "144"], ["1973", "20th", "46.2%", "68", "144"], ["1977", "21st", "50.6%", "84", "148"], ["1981", "22nd", "45.3%", "77", "166"], ["1982 (feb)", "23rd", "47.3%", "81", "166"], ["1982 (nov)", "24th", "45.2%", "75", "166"], ["1987", "25th", "44.2%", "81", "166"], ["1989", "26th", "44.2%", "77", "166"], ["1992", "27th", "39.1%", "68", "166"], ["1997", "28th", "39.3%", "77", "166"], ["2002", "29th", "41.5%", "81", "166"], ["2007", "30th", "41.6%", "77", "166"], ["2011", "31st", "17.4%", "20", "166"]]}, "native_split": "test", "question": "How many elections did the party win75 or more seats?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "13", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "66ea61b936831554ebaa4423d2600550"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:87d27673443cdd977986ee4fe7ece98b", "source": "tablebench", "table_key": "1eb5c2d4d282c32ef0746ade8a901b454afce732", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 8, "section_rows": [], "cells": [["type", "beam height (mm)", "flange width (mm)", "web thickness (mm)", "flange thickness (mm)", "weight (kg / m)", "cross - section area (cm 2 )", "moment of inertia in torsion (j) (cm 4 )"], ["ismb 80", "80", "46", "3.8", "5.2", "6.0", "7.64", "0.7"], ["ismb 100", "100", "55", "4.1", "5.7", "8.1", "10.3", "1.1"], ["ismb 120", "120", "70", "4.4", "6.3", "10.4", "13.2", "1.71"], ["ismb 140", "140", "73", "4.7", "6.9", "12.9", "16.4", "2.54"], ["ismb 750 137", "753", "263", "11.5", "17.0", "137.0", "175.0", "137.1"], ["ismb 750 147", "753", "265", "13.2", "17.0", "147.0", "188.0", "161.5"], ["ismb 750 173", "762", "267", "14.4", "21.6", "173.0", "221.0", "273.6"]]}, "native_split": "test", "question": "Which beam type has the highest moment of inertia in torsion (j) among all the options?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "ismb 750 173", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "87d27673443cdd977986ee4fe7ece98b"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:412f39ec15ed20c84370029b2ff39ce5", "source": "tablebench", "table_key": "0c90f1a7f5afb2392a4db65f1901b40a98a7d796", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 5, "section_rows": [], "cells": [["index", "organization", "year", "rank", "out of"], ["bribe payers index", "transparency international", "2011", "19", "28"], ["corruption perceptions index", "transparency international", "2012", "37", "176"], ["democracy index", "economist intelligence unit", "2010", "36", "167"], ["ease of doing business index", "world bank", "2012", "16", "185"], ["economic freedom index", "fraser institute", "2010", "15", "144"], ["economic freedom index", "the heritage foundation", "2013", "20", "177"], ["global competitiveness report", "world economic forum", "20122013", "13", "144"], ["global peace index", "institute for economics and peace", "2011", "27", "153"], ["globalization index", "at kearney / foreign policy magazine", "2006", "35", "62"], ["press freedom index", "reporters without borders", "2013", "47", "179"], ["property rights index", "property rights alliance", "2008", "28", "115"]]}, "native_split": "test", "question": "In which year did the organization 'Transparency International' publish the highest-ranked index?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2011", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "412f39ec15ed20c84370029b2ff39ce5"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:7185f1c04eafa4e732031ebb4258d7bf", "source": "tablebench", "table_key": "25c492ac52505fd03cc20c7621752c718fa2de6b", "grid": {"TD": 1, "LD": 0, "nrows": 7, "ncols": 6, "section_rows": [], "cells": [["year", "property taxes", "investment earnings", "other local sources", "state & federal", "total revenue"], ["2005", "24384901", "255041", "2670060", "13581968", "40891700"], ["2004", "21099214", "181103", "2624131", "13999169", "37903617"], ["2003", "17199210", "509862", "2309087", "12794922", "32539572"], ["2002", "14359199", "879398", "2168096", "15132879", "32539572"], ["2001", "11631227", "1949885", "1987436", "12929489", "28498037"], ["2000", "10608734", "493839", "2127480", "8549565", "21779618"]]}, "native_split": "test", "question": "In which year did the entity experience the largest difference between 'property taxes' and 'investment earnings'?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "2005", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "7185f1c04eafa4e732031ebb4258d7bf"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:c3ce5811d8041e247d700aa708d16934", "source": "tablebench", "table_key": "b1402cc1432802bbb6f90833804dfa080c81ae58", "grid": {"TD": 1, "LD": 0, "nrows": 14, "ncols": 6, "section_rows": [], "cells": [["Year", "-", "Year", "-", "Year", "-"], ["1820", "8,385", "1885", "395,346", "1950", "249,187"], ["1825", "10,199", "1890", "455,302", "1955", "237,790"], ["1830", "23,322", "1895", "258,536", "1960", "265,398"], ["1835", "45,374", "1900", "448,572", "1965", "296,697"], ["1840", "84,066", "1905", "1,026,499", "1970", "373,326"], ["1845", "114,371", "1910", "1,041,570", "1975", "385,378"], ["1850", "369,980", "1915", "326,700", "1980", "524,295"], ["1855", "200,877", "1920", "430,001", "1985", "568,149"], ["1860", "153,640", "1925", "294,314", "1990", "1,535,872"], ["1865", "248,120", "1930", "241,700", "1995", "720,177"], ["1870", "387,203", "1935", "34,956", "2000", "841,002"], ["1875", "227,498", "1940", "70,756", "2005", "1,122,257"], ["1880", "457,257", "1945", "38,119", "2010", "1,042,625"]]}, "native_split": "test", "question": "What is the value in the year with the highest increase in value between 1850 and 1870?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "387203", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c3ce5811d8041e247d700aa708d16934"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:f19e3c32d88171eb59e66dc31967bf2d", "source": "tablebench", "table_key": "67fc1403fdf16c3c3d9dbded015494978701b52f", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 14, "section_rows": [], "cells": [["Unnamed: 0", "1994 general", "1995 regional", "1996 general", "1999 european", "2000 regional", "2001 general", "2004 european", "2005 regional", "2006 general", "2008 general", "2009 european", "2010 regional", "2013 general"], ["piedmont", "with fi", "3.0", "4.4", "3.3", "4.5", "3.5", "5.0", "4.6", "6.2", "5.2", "6.1", "3.9", "1.2"], ["lombardy", "with fi", "2.2", "4.6", "3.5", "4.1", "3.4", "3.6", "3.8", "5.9", "4.3", "5.0", "3.8", "1.1"], ["veneto", "with fi", "3.6", "5.4", "5.4", "6.8", "5.0", "5.0", "6.4", "7.8", "5.6", "6.4", "4.9", "1.7"], ["emilia - romagna", "with fi", "4.8", "4.8", "2.7", "3.7", "3.4", "2.8", "3.9", "5.8", "4.3", "4.7", "3.8", "1.1"], ["tuscany", "with fi", "2.5", "4.8", "3.2", "4.2", "3.3", "3.3", "3.7", "5.9", "4.2", "4.6", "4.8", "1.1"], ["lazio", "with fi", "4.2", "4.7", "4.8", "6.7", "4.8", "7.1", "7.8", "6.9", "4.8", "5.5", "6.1", "1.5"], ["campania", "with fi", "9.7", "8.0", "6.8", "8.5", "7.5", "7.0", "6.7", "6.8", "6.5", "8.7", "9.4", "3.6"], ["apulia", "with fi", "5.6", "7.6", "6.0", "6.2", "6.8", "8.1", "7.8", "7.8", "7.9", "9.1", "6.5", "2.0"], ["calabria", "with fi", "9.0", "9.0", "9.4", "13.3", "9.5", "9.6", "10.4", "7.7", "8.2", "9.3", "9.4", "4.1"], ["sicily", "with fi", "19.0 (1996)", "8.1", "7.9", "24.3 (2001)", "14.4", "14.0", "18.7 (2006)", "10.0", "9.4", "11.9", "12.5 (2008)", "2.8"]]}, "native_split": "test", "question": "What is the average value of the 2001 general election across all regions in Italy?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "6.16", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f19e3c32d88171eb59e66dc31967bf2d"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:e64c2ddce62c76ba41e5c576b72b1ac4", "source": "tablebench", "table_key": "ff38a284f881839688c29ac79374b58e24c14b89", "grid": {"TD": 1, "LD": 0, "nrows": 25, "ncols": 8, "section_rows": [], "cells": [["rank", "rank fortune 500", "name", "headquarters", "revenue (millions)", "profit (millions)", "employees", "industry"], ["1", "17", "sinopec", "beijing", "131636.0", "3703.1", "681900", "oil"], ["2", "24", "china national petroleum", "beijing", "110520.2", "13265.3", "1086966", "oil"], ["3", "29", "state grid corporation", "beijing", "107185.5", "2237.7", "1504000", "utilities"], ["4", "170", "industrial and commercial bank of china", "beijing", "36832.9", "6179.2", "351448", "banking"], ["5", "180", "china mobile limited", "beijing", "35913.7", "6259.7", "130637", "telecommunications"], ["6", "192", "china life insurance", "beijing", "33711.5", "173.9", "77660", "insurance"], ["7", "215", "bank of china", "beijing", "30750.8", "5372.3", "232632", "banking"], ["8", "230", "china construction bank", "beijing", "28532.3", "5810.3", "297506", "banking"], ["9", "237", "china southern power grid", "guangzhou", "27966.1", "1074.1", "178053", "utilities"], ["10", "275", "china telecom", "beijing", "24791.3", "2279.7", "400299", "telecommunications"], ["11", "277", "agricultural bank of china", "beijing", "24475.5", "728.4", "452464", "banking"], ["12", "290", "hutchison whampoa", "hong kong", "23661.0", "2578.3", "220000", "various sectors"], ["13", "299", "sinochem corporation", "beijing", "23109.2", "344.7", "20343", "various sectors"], ["14", "307", "baosteel", "shanghai", "22663.4", "1622.2", "91308", "steel"], ["15", "342", "china railway engineering", "beijing", "20520.4", "142.6", "275866", "railway"], ["16", "384", "china railway construction", "beijing", "18735.7", "70.2", "245540", "railway"], ["17", "385", "first automotive works", "changchun", "18710.7", "70.0", "136010", "automobile"], ["18", "396", "china state construction", "beijing", "18163.2", "281.3", "294309", "construction"], ["19", "402", "saic motor", "shanghai", "18010.1", "89.7", "72416", "automobile"], ["20", "405", "cofco limited", "beijing", "17953.2", "281.0", "82481", "various sectors"], ["21", "435", "china minmetals", "beijing", "16902.2", "154.4", "32594", "metal trading"], ["22", "457", "jardine matheson", "hong kong / hamilton", "16281.0", "1348.0", "240000", "various sectors"], ["23", "469", "china national offshore oil", "beijing", "16038.9", "3007.1", "44000", "oil"], ["24", "488", "china ocean shipping", "beijing", "15413.5", "1092.9", "79616", "shipping"]]}, "native_split": "test", "question": "What is the total revenue of all companies in the oil industry?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "838478.3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "e64c2ddce62c76ba41e5c576b72b1ac4"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:f98c79fd3e60a413ecc94008e44c91b3", "source": "tablebench", "table_key": "a57a6c9482147b7bff6390b3d70f3ec1c93a3f93", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 10, "section_rows": [], "cells": [["station name", "2002 - 03", "2004 - 05", "2005 - 06", "2006 - 07", "2007 - 08", "2008 - 09", "2009 - 10", "2010 - 11", "2011 - 12"], ["devonport", "18795", "16202", "18573", "19655", "17450", "21652", "21674", "27006", "27756"], ["dockyard", "4070", "5088", "4895", "5335", "4924", "5280", "5524", "5406", "7716"], ["keyham", "8957", "6374", "7594", "7976", "5050", "5648", "5016", "6330", "7708"], ["st budeaux victoria road", "5451", "5818", "6146", "5264", "5193", "5678", "7026", "6942", "7780"], ["bere ferrers", "17808", "12862", "11459", "10824", "10824", "11580", "12606", "14374", "15020"], ["bere alston", "37944", "29552", "27263", "26866", "28936", "32454", "36272", "41666", "44792"], ["calstock", "25739", "24024", "21123", "23476", "26825", "31168", "33368", "33198", "32456"], ["gunnislake", "39009", "37190", "43885", "43676", "48747", "49070", "51424", "50218", "52116"]]}, "native_split": "test", "question": "Which two stations have the smallest difference in passenger numbers between '2005 - 03' and '2008 - 09'?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "bere ferrers", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "f98c79fd3e60a413ecc94008e44c91b3"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:ef1ef44158bf1967bb2671216a01b4a9", "source": "tablebench", "table_key": "4a401ccf5496b49a16e47df30b57c733d8b76167", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 6, "section_rows": [], "cells": [["year", "number of tropical storms", "number of hurricanes", "number of major hurricanes", "deaths", "strongest storm"], ["1860", "1", "5", "1", "60 +", "one"], ["1861", "2", "6", "0", "22 +", "one and three"], ["1862", "3", "3", "0", "3", "two and three"], ["1863", "4", "5", "0", "90", "one , two , three & four"], ["1864", "2", "3", "0", "none", "one , three & five"], ["1865", "4", "3", "0", "326", "four & seven"], ["1866", "1", "5", "1", "383", "six"], ["1867", "2", "6", "0", "811", "'san narciso'"], ["1868", "1", "3", "0", "2", "one , two & four"]]}, "native_split": "test", "question": "In the context of tropical storms, the Hurricane Severity Index (HSI) is calculated as the sum of the number of hurricanes and major hurricanes, divided by the total number of tropical storms, indicating the severity of the storm season. What year had the highest Hurricane Severity Index?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1860, 1866", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ef1ef44158bf1967bb2671216a01b4a9"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:b08634d7cd884455337211051bbfc115", "source": "tablebench", "table_key": "eca93f436caa0c2aadc443030defcbdce20197ac", "grid": {"TD": 1, "LD": 0, "nrows": 10, "ncols": 5, "section_rows": [], "cells": [["crime", "reported offenses", "killeen rate", "texas rate", "us rate"], ["murder", "10", "8.6", "5.6", "5.6"], ["rape", "66", "56.9", "32.9", "29.4"], ["robbery", "216", "186.4", "155.2", "154.0"], ["aggravated assault", "593", "511.6", "314.4", "281.6"], ["violent crime", "885", "763.5", "508.2", "470.6"], ["burglary", "1711", "1476.2", "946.5", "743.4"], ["larceny - theft", "2877", "2482.2", "2688.9", "2200.1"], ["motor vehicle theft", "169", "145.8", "351.1", "330.5"], ["non - violent crime", "4757", "4104.2", "3986.6", "3274.0"]]}, "native_split": "test", "question": "How many types of violent crimes are reported in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "4", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b08634d7cd884455337211051bbfc115"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:394939e9fa1967642597a49620069b78", "source": "tablebench", "table_key": "f9db4c3b0399aaa885622fa680f3f9f83e47e5ba", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 8, "section_rows": [], "cells": [["rank", "company", "country", "industry", "sales (billion )", "profits (billion )", "assets (billion )", "market value (billion )"], ["1", "citigroup", "usa", "banking", "108.28", "17.05", "1 , 4.10", "247.66"], ["2", "general electric", "usa", "conglomerates", "152.36", "16.59", "750.33", "372.14"], ["3", "american international group", "usa", "insurance", "95.04", "10.91", "776.42", "173.99"], ["4", "bank of america", "usa", "banking", "65.45", "14.14", "1110.46", "188.77"], ["5", "hsbc", "uk", "banking", "62.97", "9.52", "1031.29", "186.74"], ["6", "exxonmobil", "usa", "oil & gas", "263.99", "25.33", "195.26", "405.25"], ["7", "royal dutch shell", "netherlands", "oil & gas", "265.19", "18.54", "193.83", "221.49"], ["8", "bp", "uk", "oil & gas", "285.06", "15.73", "191.11", "231.88"], ["9", "ing group", "netherlands", "diversified financials", "92.01", "8.1", "1175.16", "68.04"], ["10", "toyota", "japan", "automotive", "165.68", "11.13", "211.15", "140.89"]]}, "native_split": "test", "question": "What is the total sales of the top 3 companies in the 'banking' industry?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "236.7", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "394939e9fa1967642597a49620069b78"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:b60f42005fcd5f0e80b5e791178df802", "source": "tablebench", "table_key": "391cb3350720443d0d45b095a0e82935d1028595", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 6, "section_rows": [], "cells": [["engine type", "scenario", "sfc in lb / (lbf h)", "sfc in g / (kn s)", "specific impulse (s)", "effective exhaust velocity (m / s)"], ["nk - 33 rocket engine", "vacuum", "10.9", "309.0", "331", "3240"], ["ssme rocket engine", "space shuttle vacuum", "7.95", "225.0", "453", "4423"], ["ramjet", "mach 1", "4.5", "127.0", "800", "7877"], ["j - 58 turbojet", "sr - 71 at mach 3.2 (wet)", "1.9", "53.8", "1900", "18587"], ["rolls - royce / snecma olympus 593", "concorde mach 2 cruise (dry)", "1.195", "33.8", "3012", "29553"], ["cf6 - 80c2b1f turbofan", "boeing 747 - 400 cruise", "0.605", "17.1", "5950", "58400"], ["general electric cf6 turbofan", "sea level", "0.307", "8.696", "11700", "115000"]]}, "native_split": "test", "question": "How many engines have a specific impulse greater than 1000 seconds?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "4", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b60f42005fcd5f0e80b5e791178df802"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:c2d0e0b5e2f5835d65687912d1ad3d7c", "source": "tablebench", "table_key": "9a7e3e906442ef22aa238e8f4d08450f8df9bbcf", "grid": {"TD": 1, "LD": 0, "nrows": 19, "ncols": 5, "section_rows": [], "cells": [["election", "of candidates nominated", "of seats won", "of total votes", "% of popular vote"], ["1945", "203", "65", "1448744", "27.62%"], ["1949", "249", "41", "1734261", "29.62%"], ["1953", "248", "50", "1749579", "31.01%"], ["1957", "256", "109", "2564732", "38.81%"], ["1958", "265", "208", "3908633", "53.56%"], ["1962", "265", "114", "2865542", "37.22%"], ["1963", "265", "93", "2582322", "32.72%"], ["1965", "265", "95", "2500113", "32.41%"], ["1968", "262", "72", "2548949", "31.36%"], ["1972", "265", "107", "3388980", "35.02%"], ["1974", "264", "95", "3371319", "35.46%"], ["1979", "282", "136", "4111606", "35.89%"], ["1980", "282", "103", "3552994", "32.49%"], ["1984", "282", "211", "6278818", "50.03%"], ["1988", "295", "169", "5667543", "43.02%"], ["1993", "295", "2", "2178303", "16.04%"], ["1997", "301", "20", "2446705", "18.84%"], ["2000", "291", "12", "1566994", "12.19%"]]}, "native_split": "test", "question": "What is the total number of seats won by the party across all elections listed in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1702", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c2d0e0b5e2f5835d65687912d1ad3d7c"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:0b5b037022d36184f582c1aaf15969ad", "source": "tablebench", "table_key": "906b24596a9fe857366a8356000af065bba39a5e", "grid": {"TD": 1, "LD": 0, "nrows": 29, "ncols": 6, "section_rows": [], "cells": [["interval name", "size (steps)", "size (cents)", "just ratio", "just (cents)", "error"], ["perfect fifth", "24", "702.44", "3:2", "701.96", "+ 0.48"], ["septimal tritone", "20", "585.37", "7:5", "582.51", "+ 2.85"], ["11:8 wide fourth", "19", "556.1", "11:8", "551.32", "+ 4.78"], ["15:11 wide fourth", "18", "526.83", "15:11", "536.95", "10.12"], ["27:20 wide fourth", "18", "526.83", "27:20", "519.55", "+ 7.28"], ["perfect fourth", "17", "497.56", "4:3", "498.04", "0.48"], ["septimal narrow fourth", "16", "468.29", "21:16", "470.78", "2.48"], ["septimal major third", "15", "439.02", "9:7", "435.08", "+ 3.94"], ["undecimal major third", "14", "409.76", "14:11", "417.51", "7.75"], ["pythagorean major third", "14", "409.76", "81:64", "407.82", "+ 1.94"], ["major third", "13", "380.49", "5:4", "386.31", "5.83"], ["inverted 13th harmonic", "12", "351.22", "16:13", "359.47", "8.25"], ["undecimal neutral third", "12", "351.22", "11:9", "347.41", "+ 3.81"], ["minor third", "11", "321.95", "6:5", "315.64", "+ 6.31"], ["pythagorean minor third", "10", "292.68", "32:27", "294.13", "1.45"], ["tridecimal minor third", "10", "292.68", "13:11", "289.21", "+ 3.47"], ["septimal minor third", "9", "263.41", "7:6", "266.87", "3.46"], ["septimal whole tone", "8", "234.15", "8:7", "231.17", "+ 2.97"], ["whole tone , major tone", "7", "204.88", "9:8", "203.91", "+ 0.97"], ["whole tone , minor tone", "6", "175.61", "10:9", "182.4", "6.79"], ["lesser undecimal neutral second", "5", "146.34", "12:11", "150.64", "4.30"], ["septimal diatonic semitone", "4", "117.07", "15:14", "119.44", "2.37"], ["diatonic semitone", "4", "117.07", "16:15", "111.73", "+ 5.34"], ["pythagorean diatonic semitone", "3", "87.8", "256:243", "90.22", "2.42"], ["septimal chromatic semitone", "3", "87.8", "21:20", "84.47", "+ 3.34"], ["chromatic semitone", "2", "58.54", "25:24", "70.67", "12.14"], ["28:27 semitone", "2", "58.54", "28:27", "62.96", "4.42"], ["septimal comma", "1", "29.27", "64:63", "27.26", "+ 2.00"]]}, "native_split": "test", "question": "What is the average size in cents of the intervals with a just ratio that involves the number 11?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "380.49", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0b5b037022d36184f582c1aaf15969ad"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:b6219d257925aa2abc3e4511e5a2ac16", "source": "tablebench", "table_key": "abadf20db5e2705094e75caa05ea1edcd4b0afc1", "grid": {"TD": 1, "LD": 0, "nrows": 29, "ncols": 5, "section_rows": [], "cells": [["states", "males (%)", "males rank", "females (%)", "females rank"], ["india", "12.1", "14", "16.0", "15"], ["punjab", "30.3", "1", "37.5", "1"], ["kerala", "24.3", "2", "34.0", "2"], ["goa", "20.8", "3", "27.0", "3"], ["tamil nadu", "19.8", "4", "24.4", "4"], ["andhra pradesh", "17.6", "5", "22.7", "10"], ["sikkim", "17.3", "6", "21.0", "8"], ["mizoram", "16.9", "7", "20.3", "17"], ["himachal pradesh", "16.0", "8", "19.5", "12"], ["maharashtra", "15.9", "9", "18.1", "13"], ["gujarat", "15.4", "10", "17.7", "7"], ["haryana", "14.4", "11", "17.6", "6"], ["karnataka", "14.0", "12", "17.3", "9"], ["manipur", "13.4", "13", "17.1", "11"], ["uttarakhand", "11.4", "15", "14.8", "14"], ["arunachal pradesh", "10.6", "16", "12.5", "19"], ["uttar pradesh", "9.9", "17", "12.0", "18"], ["jammu and kashmir", "8.7", "18", "11.1", "5"], ["bihar", "8.5", "19", "10.5", "29"], ["nagaland", "8.4", "20", "10.2", "22"], ["rajasthan", "8.4", "20", "9.0", "20"], ["meghalaya", "8.2", "22", "8.9", "26"], ["orissa", "6.9", "23", "8.6", "25"], ["assam", "6.7", "24", "7.8", "21"], ["chattisgarh", "6.5", "25", "7.6", "27"], ["west bengal", "6.1", "26", "7.1", "16"], ["madhya pradesh", "5.4", "27", "6.7", "23"], ["jharkhand", "5.3", "28", "5.9", "28"]]}, "native_split": "test", "question": "What is the average percentage of males across all states in India?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "12.82", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "b6219d257925aa2abc3e4511e5a2ac16"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:19f5fcd6b29df032293fab57a9cd5e91", "source": "tablebench", "table_key": "e08d2972ed18d4220e72153277c81a2fb240a484", "grid": {"TD": 1, "LD": 0, "nrows": 22, "ncols": 5, "section_rows": [], "cells": [["year", "births (000s)", "deaths", "natural growth", "total fertility rate"], ["1990", "0.7", "0.4", "0.3", "1.58"], ["1991", "2.4", "1.85", "0.55", "1.31"], ["1992", "3.4", "2.7", "0.7", "1.33"], ["1993", "4.6", "3.3", "1.3", "1.52"], ["1994", "5.8", "4.0", "1.8", "1.65"], ["1995", "6.75", "4.6", "2.15", "1.72"], ["1996", "7.5", "5.0", "2.5", "1.70"], ["1997", "8.2", "5.4", "2.8", "1.71"], ["1998", "8.9", "5.9", "3.0", "1.71"], ["1999", "9.3", "6.3", "3.0", "1.63"], ["2000", "10.1", "6.7", "3.4", "1.62"], ["2001", "10.3", "6.9", "3.4", "1.56"], ["2002", "10.6", "7.2", "3.4", "1.55"], ["2003", "11.1", "7.25", "3.85", "1.60"], ["2004", "10.9", "7.4", "3.5", "1.55"], ["2005", "11.0", "7.6", "3.4", "1.55"], ["2006", "11.2", "7.6", "3.6", "na"], ["2007", "10.3", "7.8", "2.5", "na"], ["2008", "11.6", "7.8", "3.8", "na"], ["2009", "11.7", "7.6", "4.1", "na"], ["1990 - 2009", "166.4", "113.3", "53.1", "na"]]}, "native_split": "test", "question": "In which year was the natural growth rate significantly different from the average natural growth rate between 1990 and 2000?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1990", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "19f5fcd6b29df032293fab57a9cd5e91"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:5c2f583f71d78d31ef4fca4f4f3e084f", "source": "tablebench", "table_key": "8ef7a8a8bf2985794d1be417a96a08bd2e63ab50", "grid": {"TD": 1, "LD": 0, "nrows": 45, "ncols": 5, "section_rows": [], "cells": [["sr no", "name of road", "passes through - district (s", "length (in km)", "mdr no"], ["1", "nahan dadahul haripurdhar", "sirmour", "87.0", "1"], ["2", "solan meenus (except state highway 6 portion)", "sirmour / solan", "98.0", "2"], ["3", "banethi rajgarh chandol", "sirmour", "127.0", "3"], ["4", "markanda bridge suketi park kala amb trilokpur", "sirmour", "21.5", "4"], ["5", "kolar bilaspur", "sirmour", "13.0", "5"], ["6", "parwanoo kasauli dharampur sabhathu solan", "solan", "65.32", "6"], ["7", "barotiwala baddi sai ramshar", "solan", "44.95", "7"], ["8", "kufri chail kandaghat", "solan / shimla", "57.0", "8"], ["9", "solan barog kumarhatti", "solan", "13.0", "9"], ["10", "dharampur kasauli", "solan", "10.5", "10"], ["11", "arki dhundan bhararighat", "solan", "18.7", "11"], ["12", "nalagarh dhabota bharatgarh", "solan", "9.4", "12"], ["13", "shogi mehli junga sadhupul", "shimla", "49.4", "13"], ["14", "mashobra bhekhalti", "shimla", "18.0", "14"], ["15", "narkanda thanadhar kotgarh bithal", "shimla", "44.0", "15"], ["16", "rampur mashnoo sarahan jeori", "shimla", "62.0", "19"], ["17", "bakrot karsog (sanarli) sainj", "mandi", "41.8", "21"], ["18", "salapper tattapani suni luhri", "mandi / shimla", "120.8", "22"], ["19", "mandi kataula bajaura", "mandi", "51.0", "23"], ["20", "mandi gagal chailchowk janjehli", "mandi", "45.8", "24"], ["21", "chailchowk gohar pandoh", "mandi", "29.6", "25"], ["22", "mandi rewalsar kalkhar", "mandi", "28.0", "26"], ["23", "nore wazir bowli", "kullu", "37.0", "28"], ["24", "kullu nagar manali (left bank)", "kullu", "39.4", "29"], ["25", "jia manikarn", "kullu", "33.5", "30"], ["26", "swarghat nainadevi bhakhra", "bilaspur / una", "55.7", "31"], ["27", "nainadevi kaula da toba", "bilaspur", "12.2", "32"], ["28", "bamta kandrour", "bilaspur", "6.7", "33"], ["29", "nagaon beri", "bilaspur / solan", "37.0", "34"], ["30", "hamirpur bhoranj jahu", "hamirpur", "30.0", "35"], ["31", "nadaun sujanpur", "hamirpur", "21.0", "36"], ["32", "barsar deothsidh", "hamirpur", "11.3", "37"], ["33", "sujanpur sandhol marhi", "hamirpur / mandi", "45.0", "38"], ["35", "una hoshiarpur bankhandi hoshairpur", "una", "15.0", "40"], ["36", "tahliwal garhshankar (hp boundary)", "una", "8.0", "41"], ["37", "bharwain chintpurni kandrori damtal", "una / kangra", "95.56", "42"], ["38", "baijnath ladbharol kandapattan", "kangra / mandi", "33.0", "43"], ["39", "gaggal chetru dharamshala mcleodganj", "kangra", "24.0", "44"], ["40", "rait charhi dharamshala", "kangra", "20.0", "45"], ["41", "kaloha pragpur dhaliara dadasiba sansarpur", "kangra", "60.92", "46"], ["42", "kandwal damtal", "kangra", "16.5", "47"], ["43", "dadh malan", "kangra", "4.0", "48"], ["44", "banikhet dalhouse khajiar", "chamba", "29.0", "49"], ["45", "chamba bharmour", "chamba", "45.0", "52"]]}, "native_split": "test", "question": "What is the difference in length (in km) between the longest road and the shortest road in the 'sirmour' district?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "114", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "5c2f583f71d78d31ef4fca4f4f3e084f"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:9b632c8dd2d708565ac1d9c18d8753f6", "source": "tablebench", "table_key": "ef19e836be3c1a2ae1e08b92be8334d7f02e5074", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["Year", "R class in service at start of year", "R1 class in service at start of year", "Quantity withdrawn", "Locomotive numbers", "Notes"], ["1931", "11", "13", "1", "1342", "-"], ["1932", "10", "13", "1", "1077", "-"], ["1934", "9", "13", "3", "1126, 1152, 1338", "-"], ["1935", "6", "13", "1", "1153", "-"], ["1937", "5", "13", "1", "1125", "-"], ["1939", "4", "13", "1", "1155", "-"], ["1941", "3", "13", "1", "1336", "-"], ["1942", "2", "13", "1", "1070", "-"], ["1943", "1", "13", "1", "1124", "-"], ["1949", "0", "13", "1", "1127", "-"], ["1955", "—", "12", "2", "31154, 31335", "-"], ["1958", "—", "10", "2", "31069, 31147", "-"], ["1959", "—", "8", "6", "31010, 31107, 31128, 31174, 31339, 31340", "-"], ["1960", "—", "2", "2", "31047, 31337", "-"]]}, "native_split": "test", "question": "If the R class in service at the start of 1931 was 11, and 1 locomotive was withdrawn, what was the number of R class in service at the end of 1931?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "10", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "9b632c8dd2d708565ac1d9c18d8753f6"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:64b77c1e7f30ca236e405528bdeac502", "source": "tablebench", "table_key": "b7d822359121d57b9fae0e5990c6ec758f7a7130", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 5, "section_rows": [], "cells": [["rank", "name", "height m ( ft )", "floors", "year"], ["1", "édifice marie - guyart", "-", "33", "1972"], ["2", "complexe jules dallaire ii", "-", "28", "2013"], ["3", "place hauteville", "-", "34", "1974"], ["4", "hôtel loews le concorde", "-", "31", "1974"], ["5", "hôtel hilton québec", "-", "28", "1974"], ["6", "édifice price", "-", "18", "1930"], ["7", "place de la capitale", "-", "21", "1974"], ["8", "le samuel - holland i", "-", "24", "1981"], ["9", "chteau frontenac", "-", "18", "1893"], ["10", "édifice d'youville", "-", "21", "1969"], ["11", "complexe jules - dallaire i", "-", "17", "2010"]]}, "native_split": "test", "question": "What is the average number of floors in buildings constructed from 1895 to 1980?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "26.57", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "64b77c1e7f30ca236e405528bdeac502"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:8d2c0f071a634bd6233252d4a2f97d91", "source": "tablebench", "table_key": "eda4bf89afe5f91359bab6c9685fec6c297f8574", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 6, "section_rows": [], "cells": [["chambering", "p1 diameter (mm)", "a external (cm 2 )", "p max ( bar )", "f bolt ( kgf )", "f bolt"], [".22 long rifle", "5.74", "0.2587", "1650", "435", "n (lbf)"], ["9x19 mm parabellum", "9.93", "0.7744", "2350", "1820", "n ( lbf )"], [".357 sig", "10.77", "0.911", "3050", "2779", "n (lbf)"], [".380 acp", "9.7", "0.739", "1500", "1130", "n (lbf)"], [".40 s&w", "10.77", "0.911", "2250", "2050", "n (lbf)"], ["10 mm auto", "10.81", "0.9178", "2300", "2111", "n (lbf)"], [".45 acp", "12.09", "1.1671", "1300", "1517", "n (lbf)"], [".454 casull", "12.13", "1.1556", "3900", "4507", "n (lbf)"]]}, "native_split": "test", "question": "Which chambering has the highest maximum pressure (p max) in bars?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": ".454 casull", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8d2c0f071a634bd6233252d4a2f97d91"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:5a02841251e9fe91955487687283aa3d", "source": "tablebench", "table_key": "a96481dd3c6b20a8f0a6a712635023b8d83b21bd", "grid": {"TD": 1, "LD": 0, "nrows": 9, "ncols": 6, "section_rows": [], "cells": [["language", "quillacollo municipality", "sipe sipe municipality", "tiquipaya municipality", "vinto municipality", "colcapirhua municipality"], ["quechua", "52399", "23819", "19308", "18630", "18050"], ["aymara", "7101", "1127", "2481", "2259", "2449"], ["guaranã­", "101", "24", "43", "38", "38"], ["another native", "82", "24", "36", "26", "46"], ["spanish", "93131", "23059", "32704", "26355", "38441"], ["foreign", "2087", "215", "1100", "403", "1136"], ["only native", "5756", "6544", "2972", "3332", "1365"], ["native and spanish", "50157", "17704", "17737", "16680", "18139"]]}, "native_split": "test", "question": "How many municipalities have a population of 40,000 or more people speaking Spanish?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "5a02841251e9fe91955487687283aa3d"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:c1657743b6eeb5b20e41af290a3dad55", "source": "tablebench", "table_key": "445f9c848c361a04e5b4c24f3d669674a563dfbb", "grid": {"TD": 1, "LD": 0, "nrows": 26, "ncols": 5, "section_rows": [], "cells": [["ensemble", "gold medals", "silver medals", "bronze medals", "total medals"], ["amador valley hs", "0", "1", "0", "1"], ["ayala high school", "4", "2", "1", "7"], ["baldwinsville hs", "2", "0", "0", "2"], ["claremont hs", "1", "1", "0", "2"], ["downers grove hs", "0", "0", "1", "1"], ["father ryan hs", "0", "1", "0", "1"], ["fort mill hs", "2", "1", "2", "5"], ["franklin central hs", "6", "0", "0", "6"], ["gateway high school", "2", "1", "1", "4"], ["goshen hs", "0", "2", "1", "3"], ["harrison central paragon hs", "0", "0", "1", "1"], ["james logan high school", "1", "1", "0", "2"], ["john overton hs", "0", "1", "2", "3"], ["king philip high school", "0", "1", "0", "1"], ["mansfield hs", "0", "1", "0", "1"], ["mission viejo high school", "0", "1", "0", "1"], ["muscle shoals hs", "1", "1", "2", "4"], ["new philadelphia hs", "0", "1", "0", "1"], ["northglenn hs", "0", "0", "1", "1"], ["rangeview hs", "0", "1", "0", "1"], ["roland hayes school", "0", "0", "1", "1"], ["tarpon springs hs", "0", "1", "0", "1"], ["tunstall hs", "0", "3", "4", "7"], ["warsaw community hs", "0", "0", "1", "1"], ["woodbridge hs", "1", "0", "0", "1"]]}, "native_split": "test", "question": "How many more total medals did the school with the highest total medals win than the school with the lowest total medals?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "6", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "c1657743b6eeb5b20e41af290a3dad55"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:8bbcb834cd1f78b5fdea62a28c9746d5", "source": "tablebench", "table_key": "a625a0f28d4c894d28bf989a016236d4dd759208", "grid": {"TD": 1, "LD": 0, "nrows": 29, "ncols": 13, "section_rows": [], "cells": [["squad no", "name", "position", "league apps", "league goals", "fa cup apps", "fa cup goals", "league cup apps", "league cup goals", "flt apps", "flt goals", "total apps", "total goals"], ["2", "andy holdsworth", "df", "43 (1)", "3", "5", "0", "0", "0", "1", "0", "49 (1)", "3"], ["3", "joe skarz", "df", "22 (5)", "0", "2 (1)", "0", "1", "0", "1", "0", "26 (6)", "0"], ["4", "michael collins", "mf", "35 (6)", "2", "3 (2)", "1", "1", "0", "1", "1", "40 (8)", "4"], ["5", "david mirfin", "df", "23 (6)", "1", "3 (1)", "0", "1", "0", "0", "0", "27 (7)", "1"], ["6", "nathan clarke", "df", "44", "2", "4", "0", "1", "0", "1", "0", "50", "2"], ["7", "chris brandon", "mf", "25 (3)", "2", "2", "1", "1", "0", "1", "0", "29 (3)", "3"], ["8", "jon worthington", "mf", "19 (6)", "0", "1", "0", "1", "0", "0", "0", "21 (6)", "0"], ["9", "danny cadamarteri", "fw", "10 (2)", "3", "1 (1)", "0", "0", "0", "0", "0", "11 (3)", "3"], ["10", "robbie williams", "df", "24 (1)", "2", "3", "0", "0", "0", "0", "0", "27 (1)", "2"], ["11", "danny schofield", "mf", "19 (6)", "2", "4 (1)", "0", "1", "0", "1", "0", "25 (7)", "2"], ["12", "tom clarke", "df", "2 (1)", "0", "0", "0", "0", "0", "0 (1)", "0", "2 (2)", "0"], ["13", "frank sinclair", "df", "28 (1)", "0", "5", "0", "1", "0", "0", "0", "34 (1)", "0"], ["14", "phil jevons", "fw", "17 (4)", "7", "3 (1)", "2", "0", "0", "0", "0", "20 (5)", "9"], ["14", "richard keogh", "df", "9", "1", "0", "0", "0", "0", "1", "0", "10", "1"], ["15", "malvin kamara", "mf", "33 (10)", "3", "3 (2)", "2", "1", "0", "1", "0", "38 (12)", "5"], ["16", "ronnie wallwork", "mf", "16", "3", "2", "0", "0", "0", "0", "0", "18", "3"], ["17", "matty young", "mf", "4 (4)", "0", "0", "0", "0", "0", "0 (1)", "0", "4 (5)", "0"], ["18", "luke beckett", "fw", "25 (11)", "8", "3 (2)", "4", "1", "0", "1", "0", "30 (13)", "12"], ["19", "aaron hardy", "df", "5 (1)", "0", "0", "0", "0 (1)", "0", "1", "0", "6 (2)", "0"], ["20", "danny racchi", "df", "0 (3)", "0", "0", "0", "0", "0", "0", "0", "0 (3)", "0"], ["21", "lucas akins", "fw", "0 (3)", "0", "0", "0", "0", "0", "0 (1)", "0", "0 (4)", "0"], ["22", "james berrett", "mf", "10 (5)", "1", "2", "0", "0", "0", "0", "0", "12 (5)", "1"], ["23", "andy booth", "fw", "28 (10)", "9", "2 (1)", "0", "0 (1)", "0", "0", "0", "30 (12)", "9"], ["27", "matt glennon", "gk", "45", "0", "5", "0", "1", "0", "1", "0", "52", "0"], ["28", "alex smithies", "gk", "1 (1)", "0", "0", "0", "0", "0", "0", "0", "1 (1)", "0"], ["29", "robert page", "df", "18", "1", "2", "0", "0", "0", "0", "0", "20", "1"], ["31", "shane killock", "df", "1", "0", "0", "0", "0", "0", "0", "0", "1", "0"], ["32", "daniel broadbent", "fw", "0 (5)", "0", "0", "0", "0", "0", "0", "0", "0 (5)", "0"]]}, "native_split": "test", "question": "What is the difference in total goals scored by the top-scoring forward (fw) and the top-scoring midfielder (mf) in the league?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "7", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8bbcb834cd1f78b5fdea62a28c9746d5"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:0e1001d55ac9d8f38aa594007e13070e", "source": "tablebench", "table_key": "27209711661169ff483774f6b35d2fe2f08d3d92", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 20, "section_rows": [], "cells": [["0", "pōlô", "Unnamed: 2", "Unnamed: 3", "Unnamed: 4", "Unnamed: 5", "Unnamed: 6", "Unnamed: 7", "Unnamed: 8", "Unnamed: 9", "Unnamed: 10", "Unnamed: 11", "Unnamed: 12", "Unnamed: 13", "Unnamed: 14", "Unnamed: 15", "Unnamed: 16", "Unnamed: 17", "Unnamed: 18", "Unnamed: 19"], ["1", "əsad", "11", "samsad", "21", "darwamsad", "31", "tolomsad", "41", "pamsad", "51", "limamsad", "61", "nəmsad", "71", "pitomsad", "81", "walomsad", "91", "yamsad"], ["2", "darwā", "12", "samdarwā", "22", "darwamdarwā", "32", "tolomdarwā", "42", "pamdarwā", "52", "limamdarwā", "62", "nəmdarwā", "72", "pitomdarwā", "82", "walomdarwā", "92", "yamdarwā"], ["3", "tolō", "13", "samtolō", "23", "darwamtolō", "33", "tolomtolō", "43", "pamtolō", "53", "limamtolō", "63", "nəmtolō", "73", "pitomtolō", "83", "walomtolō", "93", "yamtolō"], ["4", "əpat", "14", "sampat", "24", "darwampat", "34", "tolompat", "44", "pampat", "54", "limampat", "64", "nəmpat", "74", "pitompat", "84", "walompat", "94", "yampat"], ["5", "limā", "15", "samlimā", "25", "darwamlimā", "35", "tolomlimā", "45", "pamlimā", "55", "limamlimā", "65", "nəmlimā", "75", "pitomlimā", "85", "walomlimā", "95", "yamlimā"], ["6", "ənəm", "16", "samnəm", "26", "darwamnəm", "36", "tolomnəm", "46", "pamnəm", "56", "limamnəm", "66", "nəmnəm", "76", "pitomnəm", "86", "walomnəm", "96", "yamnəm"], ["7", "pitō", "17", "sampitō", "27", "darwampitō", "37", "tolompitō", "47", "pampitō", "57", "limampitō", "67", "nəmpitō", "77", "pitompitō", "87", "walompitō", "97", "yampitō"], ["8", "walō", "18", "samwalō", "28", "darwamwalō", "38", "tolomwalō", "48", "pamwalō", "58", "limamwalō", "68", "nəmwalō", "78", "pitomwalō", "88", "walomwalō", "98", "yamwalō"], ["9", "siyam", "19", "samsiyam", "29", "darwamsiyam", "39", "tolomsiyam", "49", "pamsiyam", "59", "limamsiyam", "69", "nəmsiyam", "79", "pitomsiyam", "89", "walomsiyam", "99", "yamsiyam"], ["10", "sampōlô", "20", "darwampōlô", "30", "tolompōlô", "40", "pampōlô", "50", "limampōlô", "60", "nəmpōlô", "70", "pitompōlô", "80", "walompōlô", "90", "yampōlô", "100", "saŋgatos"]]}, "native_split": "test", "question": "How many rows have a value in the 'pōlô' column that starts with the letter 'ə'?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "3", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0e1001d55ac9d8f38aa594007e13070e"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:0595618d0746974966d38bb82fc99faf", "source": "tablebench", "table_key": "49a1f6bb1c8f12c6486e3eff0c3b7be1260f3fc1", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 10, "section_rows": [], "cells": [["Unnamed: 0", "episode", "air date", "rating", "share", "rating / share 1849", "viewers (m)", "timeslot rank", "night rank", "overall rank"], ["1", "pilot", "tuesday , march 4 , 2008", "8.2", "12", "4.5 / 11", "13.47", "1", "2", "6"], ["2", "golden boy", "thursday , march 6 , 2008", "6.2", "10", "3.5 / 8", "10.12", "2", "4", "15"], ["3", "soldier 's heart", "monday , march 10 , 2008", "5.5", "8", "2.5 / 6", "8.78", "3", "6", "20"], ["4", "honor", "monday , march 17 , 2008", "4.5", "7", "2.3 / 6", "7.3", "4", "10", "37"], ["5", "keep the change", "monday , march 24 , 2008", "3.8", "6", "2.0 / 5", "6.19", "4", "11", "52"], ["6", "legacy", "monday , march 31 , 2008", "4.3", "6", "2.1 / 5", "6.63", "4", "10", "43"], ["7", "reclassified", "monday , april 7 , 2008", "4.6", "7", "2.2 / 5", "7.44", "4", "n / a", "37"]]}, "native_split": "test", "question": "What is the average rating of episodes that have a viewership of at least 10 million and are ranked within the top 3 in their timeslot?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "7.2", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "0595618d0746974966d38bb82fc99faf"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:8c3668697e8601047edef065555855fc", "source": "tablebench", "table_key": "769068a22dd0408ce86b29733a2d98c0c4e11df5", "grid": {"TD": 1, "LD": 0, "nrows": 12, "ncols": 5, "section_rows": [], "cells": [["Party", "First Duma", "Second Duma", "Third Duma", "Fourth Duma"], ["Russian Social Democratic Party", "18 (Mensheviks)", "47 (Mensheviks)", "19 (Bolsheviks)", "15 (Bolsheviks)"], ["Socialist-Revolutionary Party", "–", "37", "–", "–"], ["Labour group", "136", "104", "13", "10"], ["Progressist Party", "27", "28", "28", "41"], ["Constitutional Democratic Party (Kadets)", "179", "92", "52", "57"], ["Non-Russian National Groups", "121", "–", "26", "21"], ["Centre Party", "–", "–", "–", "33"], ["Octobrist Party", "17", "42", "154", "95"], ["Nationalists", "60", "93", "26", "22"], ["Rightists", "8", "10", "147", "154"], ["TOTAL", "566", "453", "465", "448"]]}, "native_split": "test", "question": "Duma Seat Share Change is defined as the variation in the percentage of seats held by each political party in the Russian Duma from one legislative period to another. Based on this definition, what is the Duma Seat Share Change of the Constitutional Democratic Party (Kadets) from the First Duma to the Second Duma?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "-87", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8c3668697e8601047edef065555855fc"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:ea29c3703787096422647ea08ceb19f9", "source": "tablebench", "table_key": "4143454e6aa5e5bd59cf1143969dd734ba96920d", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 6, "section_rows": [], "cells": [["Unnamed: 0", "district", "province", "region", "ubigeo", "elevation (m)"], ["1", "suykutambo", "espinar", "cusco", "80807", "4801"], ["2", "condoroma", "espinar", "cusco", "80802", "4737"], ["3", "san antonio", "puno", "puno", "210113", "4700"], ["4", "ananea", "san antonio de putina", "puno", "211002", "4660"], ["5", "morococha", "yauli", "junín", "120805", "4550"], ["6", "san antonio de chuca", "caylloma", "arequipa", "40514", "4525"], ["7", "santa ana", "castrovirreyna", "huancavelica", "90411", "4473"], ["8", "marcapomacocha", "yauli", "junín", "120804", "4415"], ["9", "capazo", "el collao", "puno", "210502", "4400"], ["10", "paratia", "lampa", "puno", "210707", "4390"], ["11", "cojata", "huancané", "puno", "210602", "4355"], ["12", "yanacancha", "pasco", "pasco", "190113", "4350"], ["13", "chaupimarca", "pasco", "pasco", "190101", "4338"], ["14", "macusani", "carabaya", "puno", "210301", "4315"], ["15", "huayllay", "pasco", "pasco", "190104", "4310"], ["16", "caylloma", "caylloma", "arequipa", "40505", "4310"], ["17", "vilavila", "lampa", "puno", "210710", "4300"], ["18", "tanta", "yauyos", "lima", "151028", "4278"], ["19", "tinyahuarco", "pasco", "pasco", "190111", "4275"]]}, "native_split": "test", "question": "What is the average elevation of districts in the Cusco region that have an elevation above 4700 meters?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "4769", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "ea29c3703787096422647ea08ceb19f9"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:1274eebbc02e9c74547f94c43fbd5cdb", "source": "tablebench", "table_key": "2024651f8e99bca287ff766888ada3fe01ee5a88", "grid": {"TD": 1, "LD": 0, "nrows": 11, "ncols": 6, "section_rows": [], "cells": [["rank", "city", "population", "area (km 2 )", "density (inhabitants / km 2 )", "altitude (mslm)"], ["1st", "alessandria", "94191", "203.97", "461.8", "95"], ["2nd", "casale monferrato", "36039", "86.32", "417.5", "116"], ["3rd", "novi ligure", "28581", "54.22", "527.1", "197"], ["4th", "tortona", "27476", "99.29", "276.7", "122"], ["5th", "acqui terme", "20426", "33.42", "611.2", "156"], ["6th", "valenza", "20282", "50.05", "405.2", "125"], ["7th", "ovada", "11912", "35.33", "337.2", "186"], ["8th", "serravalle scrivia", "6445", "16.02", "402.3", "225"], ["9th", "arquata scrivia", "6260", "30.36", "206.2", "248"], ["10th", "castelnuovo scrivia", "5473", "45.42", "120.5", "85"]]}, "native_split": "test", "question": "What is the total population of the top 5 cities in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "206713", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "1274eebbc02e9c74547f94c43fbd5cdb"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:aca822dccfa5b7a04abe4dd08ba88e50", "source": "tablebench", "table_key": "eeac8cfec4e1a101e419cc88c19bbf086c29402d", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 5, "section_rows": [], "cells": [["#", "Name", "Birth and death", "Office started", "Office ended"], ["1", "Geir Vídalín", "27 October 1761\n–\n20 September 1823", "1801", "1823"], ["2", "Steingrímur Jónsson", "None", "1824", "1845"], ["3", "Helgi Thordersen", "8 April 1794\n–\n4 December 1867", "1846", "1866"], ["4", "'Pétur Pétursson", "3 October 1808\n–\n15 May 1891", "1866", "1889"], ["5", "Hallgrímur Sveinsson", "5 April 1841\n–\n16 December 1909", "1889", "1908"], ["6", "Þórhallur Bjarnarson", "2 December 1855\n–\n15 December 1916", "1908", "1916"], ["7", "Jón Helgason", "1866\n–\n1942", "1917", "1939"], ["8", "Sigurgeir Sigurðsson", "3 August 1890\n-\n13 October 1953", "1939", "1953"], ["9", "Ásmundur Guðmundsson", "6 October 1888\nReykholt\n–\n29 May 1969\nReykjavík", "1954", "1989"], ["10", "Sigurbjörn Einarsson", "30 June 1911\nVestur-Skaftafellssýsla\n–\n28 August 2008\nReykjavík", "1959", "1981"], ["11", "Pétur Sigurgeirsson\n(son of Sigurgeir Sigurðsson, 8th Bishop of Iceland)", "2 June 1919\n–\n3 June 2010", "1981", "1989"], ["12", "Ólafur Skúlason", "29 December 1929\n–\n9 June 2008", "1989", "1997"], ["13", "Karl Sigurbjörnsson\n(son of Sigurbjörn Einarsson, 10th Bishop of Iceland)", "5 February 1947\nReykjavík", "1998", "2012"], ["14", "Agnes Sigurðardóttir", "19 October 1954\nÍsafjörður", "24 June 2012", "Incumbent"]]}, "native_split": "test", "question": "Which bishop had the longest tenure in office, and how is the difference compared to the average tenure of all the bishops?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "Ásmundur Guðmundsson, 16.85", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "aca822dccfa5b7a04abe4dd08ba88e50"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:432da69c8bf6e4c02ea05c70a4369a81", "source": "tablebench", "table_key": "4c5932fb263d660d777503e07fb72a1fd7dc5be8", "grid": {"TD": 1, "LD": 0, "nrows": 13, "ncols": 5, "section_rows": [], "cells": [["fuel gas", "upper index kcal / nm 3", "lower index kcal / nm 3", "upper index mj / nm 3", "lower index mj / nm 3"], ["hydrogen", "11528", "9715", "48.23", "40.65"], ["methane", "12735", "11452", "53.28", "47.91"], ["ethane", "16298", "14931", "68.19", "62.47"], ["ethylene", "15253", "14344", "63.82", "60.01"], ["natural gas", "12837", "11597", "53.71", "48.52"], ["propane", "19376", "17817", "81.07", "74.54"], ["propylene", "18413", "17180", "77.04", "71.88"], ["n - butane", "22066", "20336", "92.32", "85.08"], ["iso - butane", "21980", "20247", "91.96", "84.71"], ["butylene - 1", "21142", "19728", "88.46", "82.54"], ["lpg", "20755", "19106", "86.84", "79.94"], ["acetylene", "14655", "14141", "61.32", "59.16"]]}, "native_split": "test", "question": "What is the difference between the upper index kcal/nm³ of propane and the lower index kcal/nm³ of propane?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "1559", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "432da69c8bf6e4c02ea05c70a4369a81"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:65e3fbcba3509cbc4e19cdf82a8c15da", "source": "tablebench", "table_key": "ff38a284f881839688c29ac79374b58e24c14b89", "grid": {"TD": 1, "LD": 0, "nrows": 25, "ncols": 8, "section_rows": [], "cells": [["rank", "rank fortune 500", "name", "headquarters", "revenue (millions)", "profit (millions)", "employees", "industry"], ["1", "17", "sinopec", "beijing", "131636.0", "3703.1", "681900", "oil"], ["2", "24", "china national petroleum", "beijing", "110520.2", "13265.3", "1086966", "oil"], ["3", "29", "state grid corporation", "beijing", "107185.5", "2237.7", "1504000", "utilities"], ["4", "170", "industrial and commercial bank of china", "beijing", "36832.9", "6179.2", "351448", "banking"], ["5", "180", "china mobile limited", "beijing", "35913.7", "6259.7", "130637", "telecommunications"], ["6", "192", "china life insurance", "beijing", "33711.5", "173.9", "77660", "insurance"], ["7", "215", "bank of china", "beijing", "30750.8", "5372.3", "232632", "banking"], ["8", "230", "china construction bank", "beijing", "28532.3", "5810.3", "297506", "banking"], ["9", "237", "china southern power grid", "guangzhou", "27966.1", "1074.1", "178053", "utilities"], ["10", "275", "china telecom", "beijing", "24791.3", "2279.7", "400299", "telecommunications"], ["11", "277", "agricultural bank of china", "beijing", "24475.5", "728.4", "452464", "banking"], ["12", "290", "hutchison whampoa", "hong kong", "23661.0", "2578.3", "220000", "various sectors"], ["13", "299", "sinochem corporation", "beijing", "23109.2", "344.7", "20343", "various sectors"], ["14", "307", "baosteel", "shanghai", "22663.4", "1622.2", "91308", "steel"], ["15", "342", "china railway engineering", "beijing", "20520.4", "142.6", "275866", "railway"], ["16", "384", "china railway construction", "beijing", "18735.7", "70.2", "245540", "railway"], ["17", "385", "first automotive works", "changchun", "18710.7", "70.0", "136010", "automobile"], ["18", "396", "china state construction", "beijing", "18163.2", "281.3", "294309", "construction"], ["19", "402", "saic motor", "shanghai", "18010.1", "89.7", "72416", "automobile"], ["20", "405", "cofco limited", "beijing", "17953.2", "281.0", "82481", "various sectors"], ["21", "435", "china minmetals", "beijing", "16902.2", "154.4", "32594", "metal trading"], ["22", "457", "jardine matheson", "hong kong / hamilton", "16281.0", "1348.0", "240000", "various sectors"], ["23", "469", "china national offshore oil", "beijing", "16038.9", "3007.1", "44000", "oil"], ["24", "488", "china ocean shipping", "beijing", "15413.5", "1092.9", "79616", "shipping"]]}, "native_split": "test", "question": "What is the average revenue of the top 5 companies in the 'banking' industry?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "30147.88", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "65e3fbcba3509cbc4e19cdf82a8c15da"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:8869ae21589a1ab50a40faf5d85d8eaf", "source": "tablebench", "table_key": "537817e5d4ce96b8a8fd4ddf033167e1cfbe889e", "grid": {"TD": 1, "LD": 0, "nrows": 8, "ncols": 5, "section_rows": [], "cells": [["member countries", "population", "area (km square)", "gdp (billion us)", "gdp per capita (us)"], ["belgium", "9052707", "30528", "58.316", "46878"], ["france", "44788852", "674843", "312.966", "40690"], ["west germany", "54292038", "248717", "400.554", "41168"], ["italy", "49476000", "301336", "265.192", "30116"], ["luxembourg", "310291", "2586", "2.938", "113533"], ["netherlands", "11186847", "41526", "83.351", "50355"], ["ec6 (1958)", "169106736", "1299536", "1123.317", "6643"]]}, "native_split": "test", "question": "What is the minimum increase in GDP per capita required for West Germany to surpass the GDP per capita of France, assuming the population of both countries remains the same?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "0", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "8869ae21589a1ab50a40faf5d85d8eaf"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:3d4ce757ccae9604c0351525427960e7", "source": "tablebench", "table_key": "2078592d8f8562894f4acebf69f9368dea4814e0", "grid": {"TD": 1, "LD": 0, "nrows": 15, "ncols": 6, "section_rows": [], "cells": [["Unnamed: 0", "total freshwater withdrawal", "per capita withdrawal", "domestic use", "industrial use", "agricultural use"], ["turkmenistan", "24.65", "5104", "2", "1", "98"], ["kazakhstan", "35.0", "2360", "2", "17", "82"], ["uzbekistan", "58.34", "2194", "5", "2", "93"], ["guyana", "1.64", "2187", "2", "1", "98"], ["hungary", "21.03", "2082", "9", "59", "32"], ["azerbaijan", "17.25", "2051", "5", "28", "68"], ["kyrgyzstan", "10.08", "1916", "3", "3", "94"], ["tajikistan", "11.96", "1837", "4", "5", "92"], ["usa", "477.0", "1600", "13", "46", "41"], ["suriname", "0.67", "1489", "4", "3", "93"], ["iraq", "42.7", "1482", "3", "5", "92"], ["canada", "44.72", "1386", "20", "69", "12"], ["thailand", "82.75", "1288", "2", "2", "95"], ["ecuador", "16.98", "1283", "12", "5", "82"]]}, "native_split": "test", "question": "Which country has the highest percentage of total freshwater withdrawal used for agricultural purposes in each country?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "turkmenistan, guyana", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "3d4ce757ccae9604c0351525427960e7"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:58c12f5c85dc3306c3e383b2ae5f130c", "source": "tablebench", "table_key": "0fbc7db870083071fd6a908146e53eda85833fd0", "grid": {"TD": 1, "LD": 0, "nrows": 20, "ncols": 5, "section_rows": [], "cells": [["city", "january (avg high degree)", "january (avg low degree)", "july (avg high degree)", "july (avg low degree)"], ["winnipeg , mb", "10.6", "6.5", "78.4", "56.3"], ["saskatoon , sk", "13.8", "5.3", "77.5", "52.9"], ["regina , sk", "15.3", "4.2", "78.2", "53.4"], ["quebec city , qc", "19.4", "3.0", "76.5", "57.2"], ["edmonton , ab", "20.7", "0.1", "73.0", "49.1"], ["ottawa , on", "21.6", "6.1", "79.9", "60.3"], ["calgary , ab", "30.4", "8.2", "73.8", "49.6"], ["montreal , qc", "22.5", "6.8", "79.3", "61.0"], ["halifax , ns", "31.8", "17.2", "73.6", "59.2"], ["st john 's , nl", "30.6", "17.2", "69.3", "51.6"], ["toronto , on", "30.7", "19.8", "79.9", "64.4"], ["windsor , on", "31.5", "18.9", "82.6", "64.2"], ["vancouver , bc", "44.2", "34.3", "71.8", "56.7"], ["kamloops , bc", "32.7", "21.4", "84.0", "57.6"], ["yellowknife , nt", "6.9", "21.1", "70.3", "54.7"], ["iqaluit , nu", "9.0", "23.6", "54.1", "39.4"], ["moncton , nb", "25.3", "6.8", "76.5", "55.2"], ["charlottetown , pei", "25.9", "10.2", "73.9", "57.4"], ["whitehorse , yt", "12.2", "2.6", "69.1", "46.4"]]}, "native_split": "test", "question": "How much higher is the average high temperature in July compared to January across all cities?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "51.93", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "58c12f5c85dc3306c3e383b2ae5f130c"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}
{"id": "tablebench:dp-test:767d183aa02457793092c2e983eeb612", "source": "tablebench", "table_key": "d990e7a9e8d3a1944111cfa895d1c716fa0b3fa1", "grid": {"TD": 1, "LD": 0, "nrows": 18, "ncols": 7, "section_rows": [], "cells": [["inegi code", "municipality", "municipal seat", "area (km 2 )", "population (2005)", "population density ( / km 2 )", "human development index (2000)"], ["1", "amealco de bonfil", "amealco", "682.1", "56457", "82.8", "0.6803"], ["2", "pinal de amoles", "pinal de amoles", "705.37", "25325", "35.9", "0.6659"], ["3", "arroyo seco", "arroyo seco", "731.17", "12493", "17.1", "0.7029"], ["4", "cadereyta de montes", "cadereyta", "1131.0", "57204", "50.6", "0.7074"], ["5", "colón", "colón", "807.15", "51625", "64.0", "0.7036"], ["6", "corregidora", "el pueblito", "245.8", "104218", "424.0", "0.8535"], ["7", "ezequiel montes", "ezequiel montes", "298.28", "34729", "116.4", "0.7534"], ["8", "huimilpan", "huimilpan", "388.4", "32728", "84.3", "0.6824"], ["9", "jalpan de serra", "jalpan", "1185.1", "22025", "18.6", "0.7178"], ["10", "landa de matamoros", "landa de matamoros", "840.1", "18905", "22.5", "0.6606"], ["11", "el marqués", "la cañada", "787.4", "79743", "101.3", "0.7295"], ["12", "pedro escobedo", "pedro escobedo", "290.9", "17007", "58.5", "0.7598"], ["13", "peñamiller", "peñamiller", "694.9", "56553", "81.4", "0.7023"], ["14", "querétaro", "santiago de querétaro", "759.9", "734139", "966.1", "0.856"], ["15", "san joaquín", "san joaquín", "499.0", "7634", "15.3", "0.6593"], ["16", "san juan del río", "san juan del río", "799.9", "208462", "260.6", "0.8035"], ["17", "tequisquiapan", "tequisquiapan", "343.6", "54929", "159.9", "0.7827"]]}, "native_split": "test", "question": "What is the average population density of all municipalities in the table?", "source_prompt": "You are a table analyst. Your task is to answer questions based on the table content.\n\n\nThe answer should follow the format below:\n[Answer Format]\nFinal Answer: AnswerName1, AnswerName2...\n\nEnsure the final answer format is the last output line and can only be in the \"Final Answer: AnswerName1, AnswerName2...\" form, no other form. Ensure the \"AnswerName\" is a number or entity name, as short as possible, without any explanation.\n\n\nGive the final answer to the question directly without any explanation.\n\nRead the table below in JSON format:\n[TABLE] \n{table}\n\nLet's get start!\nQuestion: {question}\n", "answer_dp": "150.55", "answer_type": "free", "answer_from": null, "paragraphs": null, "scale": null, "scoring": "exact", "meta": {"source_local_id": "767d183aa02457793092c2e983eeb612"}, "dataset": "tablebench", "subtype": "NumericalReasoning"}