Clarify canonical task ownership and source lineage

README.md

@@ -36,13 +36,15 @@ Google DeepMind and Google Research designed the original benchmark to measure a
 | --- | --- | ---: | --- |
 | `factual-v1` | `factual-v1` | 831 | Unchanged publisher questions of at most 128 Unicode code points, with reference answers and URLs |
 
+This dataset owns the factual retrieval inputs used alongside [LexBench-Web-Search](https://huggingface.co/datasets/Lexmount/LexBench-Web-Search). Web Search references these definitions through its composition descriptor; it does not publish another copy of the questions or source receipt.
+
 ## Selection and changes
 
 The `factual-v1` configuration contains every question in the pinned 1,000-row publisher CSV whose `problem` has at most 128 Unicode code points, measured by Python's `len()` on the decoded string. This selects 831 questions; longer questions are excluded before collection. There is no truncation, rewriting or selection based on search results.
 
 Publisher row order, question text and reference answer text are retained. Case IDs use `sqv-` followed by the publisher's `original_index`, preserving the link to OpenAI SimpleQA. Supporting URLs are split from the CSV field, deduplicated in order, and stripped of unmatched trailing closing parentheses. Topic and answer-type labels are retained. The publisher's `multi_step` and `requires_reasoning` fields are not part of these derived task records.
 
-[The source manifest](definitions/factual-source.json) records the exact publisher CSV revision and SHA-256, selection rule and expected counts. [The selection implementation](https://github.com/lexmount/browser-eval/blob/f488414a6610a9871f3a6d2d8615b58090f16184/lexbench_websearch/simpleqa.py) rebuilds the population from that input. This release contains no search-provider rankings, relevance grades or measured results.
+[The source manifest](definitions/factual-source.json) records the exact publisher CSV revision and SHA-256, selection rule and expected counts. [The selection implementation](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/src/lexbench_websearch/simpleqa.py) rebuilds the population from that input. This release contains no search-provider rankings, relevance grades or measured results.
 
 ## Data and schema
 
@@ -74,7 +76,7 @@ query = task["query"]
 answer, supporting_urls = task["reference_answer"], task["reference_urls"]
 ```
 
-`definitions.tar.gz` contains the same definition bytes as `definitions/`. Browser Eval's [data lock](https://github.com/lexmount/browser-eval/blob/f488414a6610a9871f3a6d2d8615b58090f16184/harness/benchmarks/data-lock.json) selects the installed snapshot. Follow the [Web Search guide](https://github.com/lexmount/browser-eval/blob/f488414a6610a9871f3a6d2d8615b58090f16184/harness/web_search/README.md) to declare a profile, collect provider responses and build a report with saved evidence. The certified collector is `scripts/run_certified_benchmark.py web-search`.
+`definitions.tar.gz` contains the same definition bytes as `definitions/`. Browser Eval's [data lock](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/harness/benchmarks/data-lock.json) selects the installed snapshot. Follow the [Web Search guide](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/harness/web_search/README.md) to declare a profile, collect provider responses and build a report with saved evidence. The certified collector is `scripts/run_certified_benchmark.py web-search`.
 
 Reference Hit@k tests whether a supplied supporting URL appears in the first k results; MRR@k measures the first such hit's reciprocal rank. Answer Match@k checks answer cues in returned text. Source references are not an exhaustive set of relevant pages, and lexical answer matches do not establish that a result correctly answers the question. Report these measures separately from graded relevance. Searchable copies of the dataset can also create contamination; Browser Eval exposes a separate contamination diagnostic while retaining the original results.
 
@@ -88,6 +90,6 @@ Cite both upstream works when using this derivative, and identify the selected d
 >
 > Wei et al. (2024). [Measuring Short-Form Factuality in Large Language Models](https://github.com/openai/simple-evals). OpenAI SimpleQA.
 
-For the harness, cite [Browser Eval: Benchmarking Browsers and Web Tools for AI Agents](https://github.com/lexmount/browser-eval/blob/f488414a6610a9871f3a6d2d8615b58090f16184/CITATION.cff).
+For the harness, cite [Browser Eval: Benchmarking Browsers and Web Tools for AI Agents](https://github.com/lexmount/browser-eval/blob/d3a474c85b0c0249b11edf3e712bcd6284d9b008/CITATION.cff).
 
 [LexBench-Web-Search](https://huggingface.co/datasets/Lexmount/LexBench-Web-Search) contains the separate scenario-query suites. [LexBench-Web-Fetch](https://huggingface.co/datasets/Lexmount/LexBench-Web-Fetch) and [LexBench-WebMainBench](https://huggingface.co/datasets/Lexmount/LexBench-WebMainBench) evaluate fetching and extraction with their own task populations.