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Required by spec section 10: every deviation from the expected counts, and every
internal contradiction found in the specification itself, is recorded here
rather than silently absorbed.
Build: `dataset3/`, sources read from `ICLR27/dataset/`.
---
## Summary
| Stage | Expected | Actual | Status |
|---|---:|---:|---|
| Raw cloze prompts | 1,783,541 | 1,783,541 | match |
| Five-model consensus | 10,601 | 10,601 | match |
| Candidate-Known Facts | 8,107 | 8,107 | match |
| Grouped (subject, relation) | 6,185 | 6,185 | match |
| Functional conflicts removed | 5 | 5 | match |
| Enriched Facts | 6,180 | 6,180 | match |
| Core-source facts | 3,705 | 3,705 | match |
| Benchmark facts | 2,592 | 2,592 | match |
| Benchmark relations | 21 | 21 | match |
| Migration duplicates removed | 10,323 | 10,323 | match |
| Evaluation queries | 44,418 | **44,416** | **deviation D1** |
| — context | 6,831 | **6,829** | **deviation D1** |
| — all other conditions | as specified | as specified | match |
Everything except D1 reproduces exactly.
---
## D1 — Two context queries expose the gold answer (defect in the source data)
**Deviation:** context 6,831 → 6,829; total 44,418 → 44,416.
Spec 9.4 requires that context "must not introduce a second valid target", and
spec 13.9 that "no gold answer is exposed in open-ended main conditions". Two
queries migrated from `eval_dataset/queryD_context.jsonl` violate both, because
the legacy distractor sampler picked an entity whose surface form contains the
gold answer:
```
Some people confuse States Reorganisation Act with Indian Act.
In which jurisdiction does States Reorganisation Act apply?
gold = India <- exposed by the distractor "Indian Act"
Taiwan (ROC) Resident Certificate is a legal term in Taiwan.
Indian Act is a legal term in Canada.
In which jurisdiction does Permanent account number apply?
gold = India <- same cause
```
Spec 10 allows a count change when "the implementation discovers a documented
defect in the old data". This is such a defect.
**Action taken.** `src/build_eval_bank.py` applies a general rule rather than a
hard-coded exception: for every open-ended main-forward query, the normalized
gold must not appear in the query text after removing all mentions of the
subject. Queries failing the rule are dropped and counted in
`stage4_report.json` as `dropped_gold_exposed`.
**To restore 44,418** exactly, delete that guard — but the two queries are
unanswerable-by-design artefacts and any model scores them correct by copying.
---
## C1 — Spec 6.2 contradicts the required benchmark size (unresolved by design)
**Not a count deviation. An internal contradiction in the specification.**
Spec 6.2 lists relations "generally unsuitable" for the main benchmark, naming
among them **developer of** and **manufacturer of**. Spec 1, 6.2 and 15
simultaneously require exactly **2,592 facts across 21 relations**.
Those two relations contribute:
| Relation | Facts |
|---|---:|
| manufacturer (P176) | 441 |
| developer (P178) | 405 |
| **total** | **846** |
Excluding them yields **1,746 facts across 19 relations**, which fails the
acceptance criteria in spec 15. Keeping them satisfies the counts but contradicts
spec 6.2's guidance.
**Action taken.** The counts win, because spec 15 states them as acceptance
criteria and spec 10 forbids silent changes. Both relations are retained and
tagged:
```json
"spec_6_2_flag": "unsuitable_named_in_spec"
```
`configs/relations.yaml` exposes `options.drop_spec_6_2_flagged`. Setting it to
`true` reproduces the spec-6.2-compliant 1,746-fact variant; the selection script
then reports the resulting size instead of failing.
**Recommendation.** Report the headline number on all 2,592 and a robustness
number on the 1,746 subset. See F1, which gives an independent empirical reason
to distrust exactly these relations.
---
## F1 — 28.9% of benchmark facts are answerable by string copying
**Finding, not a deviation. No counts changed.**
For 750 of 2,592 benchmark facts (28.9%) the object string is literally contained
in the subject name, so the query can be answered by copying a substring instead
of retrieving knowledge:
```
Airbus A318 -> manufacturer Airbus
Adobe Acrobat -> developer Adobe
Amazon Music -> owned by Amazon
Agriculture and Agri-Food Canada -> jurisdiction Canada
```
Concentration by relation:
| Relation | Flagged | Total | Share |
|---|---:|---:|---:|
| manufacturer | 387 | 441 | 87.8% |
| developer | 199 | 405 | 49.1% |
| owned_by | 52 | 89 | 58.4% |
| applies_to_jurisdiction | 31 | 54 | 57.4% |
| sport | 17 | 81 | 21.0% |
| country | 17 | 263 | 6.5% |
| headquarters_location | 15 | 132 | 11.4% |
| (10 further relations) | 32 | — | <34% |
586 of the 750 come from `manufacturer` and `developer` — the two relations spec
6.2 already called unsuitable. F1 is therefore independent empirical support for
C1's recommendation.
**Why this matters.** A stability benchmark measures whether a *retrieved* fact
survives rephrasing. A fact answerable by copying is stable for a reason that has
nothing to do with knowledge, so it inflates every retention number and dampens
the very effect the study is about.
**Action taken.** Spec 16 forbids silently dropping facts, so all 750 are kept
and flagged on both the fact and the query record:
```json
"answer_in_subject_surface": true
```
Downstream analysis should report retention with and without them.
---
## F2 — 808 of the 8,107 candidate rows are exact duplicates
**Finding, not a deviation.**
The 8,107 Candidate-Known Facts contain only 7,299 distinct
`(subject, relation, object, source)` tuples; 808 rows are exact repeats, e.g.
`("BMW 3 Series", P176, "BMW", counterfact)` appears twice. This is inherited
from `dataset/filter/prompts.jsonl`.
No action needed: spec 5.1 grouping absorbs them, which is part of why 8,107 rows
become 6,185 groups. Recorded so the 8,107 figure is not mistaken for 8,107
distinct facts.
---
## N1 — Stage I GPU passes were not re-executed
The five-model first-token forward passes (1,783,541 prompts x 5 models) were
run previously; their per-prompt verdict vectors are stored as
`dataset/filter/correct_<model>.npy`.
`src/build_candidate_known.py` re-derives the candidate set from those vectors:
it re-reads the prompt table, re-computes the intersection, re-applies the
source-drop rule, and checksums every input into `stage1_report.json`. It does
not re-run inference.
The counts are verified, not assumed — 1,783,541 / 10,601 / 8,107 all reproduce,
and the result is checked for set equality against
`dataset/filter/global_known_clean.jsonl`.
`src/filter_run.py` regenerates the vectors from scratch when required.
---
## N2 — Triple direction is not normalized for `capital_of`
Spec 2.2 requires a single triple direction, forward being
`subject + relation -> object`. The relation `capital_of` (P1376) is stored the
other way round: subject = city, object = country.
Normalizing it would fold those 125 facts into `capital`, changing both the
benchmark size and the relation count, which spec 10 forbids doing silently.
**Action taken.** `capital_of` keeps `"direction": "inverse"` and
`"inverse_of": "capital"` in its record, so any analysis can group or separate
the two directions explicitly. `configs/relations.yaml` exposes
`options.normalize_inverse_direction` for the alternative build.
---
## Coverage gaps (spec 12, expected and unchanged)
Main-forward families do not cover every fact:
| Condition | Facts covered | Missing |
|---|---:|---:|
| anchor | 2,592 | 0 |
| paraphrase | 2,592 | 0 |
| format | 2,592 | 0 |
| context | 2,591 | 1 |
| multilingual | 2,402 | 190 |
Missing fact ids are listed in `outputs/coverage_report.json`. No query was
imputed. Downstream analyses that compare conditions per fact should use the
explicit valid intersection.
|