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179-annotated
Chain-of-thought (CoT) reasoning annotations for aero-engine turbine-blade defect inspection (AeBAD)
— 2,160 items (1,011 good + 1,149 defective), the reasoning-augmented sibling of
179-grounding /
179-region /
179-mcq, derived from
AI4Manufacturing/179.
Task
Grade each blade good or defective; if defective, name every defect type and its coarse region. Defect classes: ablation, breakdown, fracture, groove.
Composition
2,160 rows — good 1,011 · defective 1,149 (ablation 169, breakdown 329, fracture 389, groove 262).
Schema (8 columns)
| field | meaning |
|---|---|
query |
student question (prose; states the closed defect-class list) |
image |
the raw inspection photo |
annot |
machine-parseable gold JSON: `{"defects": [{"region", "type"}, ...], "label": "good" |
reasoning |
teacher chain-of-thought ending FINAL ANSWER: ... |
mask |
pixel GT mask (defective rows) |
cate / task |
B / T-B2 |
metadata |
source, category, image_sha256, image_path, split, … |
How the reasoning was made
- Teacher:
gpt-5.4-mini(OpenAI Batch API), English, gold-conditioned rationalization — the teacher writes forward reasoning that lands on the given human gold; it never re-solves the image and never rejection-samples the answer. The gold is authoritative; the reasoning explains it. - No leakage: the prompt forbids naming any grounding artifact (mask / ground-truth / reference / coordinates / "the given answer"); a regex gate confirms 0 leaked-vocabulary hits.
Roles
Roles: reasoning is the SFT imitation target — its FINAL ANSWER segment is the model-facing answer format; annot is the machine-parseable gold used for verification and reward parsing, not an output-format target.
Grounded traces
The reasoning_grounded column was removed 2026-07-21 per the corpus grounded-trace doctrine
(decided 2026-07-18): for classification-shape tasks the trace can only restate the verdict (with
unrequested coordinates) — geometry stays in metadata/mask; traces are pure functions of the GT and the
committed generator (forge_model annotate/) and can be re-rendered on demand. Default SFT
view: query -> reasoning. RLVR uses query + annot.
Faithfulness gate (Stage 7)
- 0 leaked-vocabulary hits across the set.
- 0 hallucinated defects on good images — the key failure mode (a clean part narrated as defective).
- Gold-conditioned faithfulness judge (
gpt-5.6-terra, a different model family from the teacher) over a 304-row good-heavy calibration sample: 1.7% flag rate, 0% on good rows. Every flagged case was either a judge conflation on a correct row or was fixed and re-verified (see the source-specific notes).
Provenance / reproduction
Built from the source dataset's human annotations by
forge_model annotate/ (builders + shared
annotate/teacher_prompt.py + annotate/cot_batch/ pipeline). Public, manual access review
(gated=manual).
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