sthanika-ai/Bharat-Knowledge-Probe-Benchmark

BKP-500 — Bharat Knowledge Probe

Does your model know where it is?

BKP-500 is a benchmark of things every Indian knows and frontier LLMs routinely fumble — lakh/crore arithmetic, Indian digit grouping, state-specific land units (bigha, katha, guntha...), traditional mass units, the Indian fiscal year, agricultural crop seasons, government schemes, and structural identifiers (PAN, GSTIN, IFSC, PIN codes).

The evaluation harness that runs a model against this dataset and grades the responses lives in a companion repository: github.com/sthanika-ai/Bharat-Knowledge-Probe-Benchmark. The exact run configuration and leaderboard for 19 models already evaluated on this dataset live in a second companion repository: github.com/sthanika-ai/BKP-500-model-runs.

Why this is a benchmark and not a trivia quiz

Most "India knowledge" evals test trivia, which frontier models are already good at and which contaminates fast. BKP-500 tests locale conventions: the arithmetic, unit, and calendar defaults that are unremarkable to a resident and invisible to a model trained mostly on US/EU text. These fail in a specific, measurable, and quietly dangerous way — a lakh/crore slip is a 100× error in a financial number, and it never looks like a refusal.

Dataset structure

The dataset ships as one JSONL file per category under data/items/, exposed as 7 Hub configs (one per category). There is no default config — the gold field's value type (int/float/str) varies across categories, so datasets can't infer one Arrow schema spanning all 552 rows at once. Pick a category config explicitly:

from datasets import load_dataset

# One category
c1 = load_dataset("sthanika-ai/Bharat-Knowledge-Probe-Benchmark", "c1_numerals", split="train")

Categories

Config	Category	Items
c1_numerals	Indian numeral system — lakh/crore ↔ million/billion, digit grouping, mixed-notation arithmetic	111
c2_weights_volumes	Traditional mass/volume units (tola, ser, maund...)	72
c3_land_units	State-specific land units (bigha, katha, guntha, kanal-marla...)	104
c4_agricultural_seasons	Crop seasons (kharif/rabi/zaid) and agricultural marketing years	91
c5_fiscal_year	Indian fiscal year and quarter arithmetic	85
c6_government_schemes	Government scheme identity, entitlements, funding splits	74
c7_structural_identifiers	PAN, GSTIN, IFSC, PIN code, vehicle registration, Aadhaar structure	15

552 rows total across the 7 categories. Load a category by name — load_dataset("sthanika-ai/Bharat-Knowledge-Probe-Benchmark", "c1_numerals") — or use load_corpus_from_hf() from the harness to pull every category in one call.

Fields

Every row shares one schema — see data/schema.json for the full JSON Schema definition. The key fields:

Field	Type	Description
id	string	Unique id, BKP-C<category>-<seq>, e.g. BKP-C3-0142.
category	string	One of the 7 categories above.
subcategory	string	Finer-grained tag within the category (e.g. magnitude_conversion, bigha_family).
prompt	string	The question text, ≤40 words.
state	string | null	2-letter Indian state/UT code, when the item is state-specific.
answer_type	string	numeric, numeric_with_unit, date, date_range, enum, string_normalized, month_set, or clarification.
gold	object	The gold answer; shape depends on answer_type.
tolerance	object | null	Grading tolerance (exact, relative, or absolute) for numeric answers.
accepted_aliases	list[string]	Accepted alternate phrasings for string-normalized answers.
grader	string	Which grader in the eval harness scores this item.
provenance	list[object]	Source, URL, quote, and confidence for the facts behind the item — see Considerations.
canary	string	Per-item canary string (BKP500-CANARY-<hex>) for contamination detection — see below.
difficulty	int	1–3 author-assigned difficulty tier.
split	string	All rows currently ship as test — see Status & caveats.

Status & caveats

• All 552 rows have passed the required two-reviewer check (adjudicated: true, split: test) and ship in plain JSONL with gold answers visible. A contamination-resistant public-dev/gated-test split is still planned but not yet built — until it lands, this repo's Hub-level access gate (see the agreement above) is the only gating in place.
• Every row carries a unique canary string (BKP500-CANARY-<hex>). If you are building a pretraining corpus and want to honor benchmark-exclusion norms, filter out any document containing one of these strings.
• Some rows — state land units especially — have provenance gaps where an authoritative source wasn't confirmed at authoring time; check provenance[].confidence before treating a fact as settled.

Considerations for using the data

• This is a locale-knowledge probe, not a general trivia set. Item design deliberately favors facts a resident would consider unremarkable (unit conversions, calendar conventions, identifier formats) over facts that require specialized domain knowledge. state/district_scope mark items whose gold answer depends on a specific jurisdiction.
• Volatile items (volatile: true, mostly in c6_government_schemes) have gold answers tied to a specific policy snapshot (as_of date) and can go stale after a Budget or scheme revision.
• Gold answers are visible. Nothing here is currently held out — don't cite scores against this corpus as evidence of a model's uncontaminated locale competence without checking whether the model's training cutoff postdates this dataset's publication.

Source data and provenance

Numeric/unit facts underlying the items are drawn from a hand-curated registry (government notifications, RBI/NSO publications, and state revenue department references) with per-row provenance recorded in each item's provenance field. See the companion GitHub repository for the registry source files and sourcing methodology notes.

Citation

@misc{bkp500,
  title  = {Bharat Knowledge Probe (BKP-500): Does Your Model Know Where It Is?},
  author = {{Sthanika AI}},
  year   = {2026},
  url    = {https://huggingface.co/datasets/sthanika-ai/Bharat-Knowledge-Probe-Benchmark}
}

License

Licensed under CC-BY-NC-4.0. If you reuse or redistribute this data, please attribute it as:

Bharat Knowledge Probe (BKP-500) dataset, © 2026 Sthanika AI, licensed under CC-BY-NC-4.0.

See LICENSE-DATA.md for the full license note. The evaluation harness code (separate repository) is licensed under Apache-2.0.

Related repositories

• Bharat-Knowledge-Probe-Benchmark — the evaluation harness (adapters, graders, scoring, statistics)
• BKP-500-model-runs — run configuration and leaderboard for 19 models already evaluated on this dataset