- llm_extract.py: match mode now window-parallel with retrieval pre-filter,
claim dedup, retry, and enable_thinking=false (vLLM) -> ~36x faster per call;
n_failed_windows/ok flags so an interrupted run never records bogus 0s.
- build_rdf_dataset.py:
- gold now includes the share-class level (hasShareClass/ticker/className)
- grounding modes alias|llm|name|context|none (--grounding); llm reads the
role-check verdicts from match_all.jsonl
- label stage: per-triple extractable + per-sample FULL/PARTIAL/NONE
- trainset stage: combines GROUNDED triples with focused TEXT EXCERPTS cut
around the actual provider statement (evidence), not the multi-MB book
- split --src to split trainset.jsonl (trust-level, no leakage)
- helper scripts: watch_match.sh, resume_match.sh (crash/sleep-safe resume),
finalize_dataset.sh
- final dataset: 335/335 trusts, 85% text<->gold agreement, 334 samples,
10,689 grounded triples, train/val/test 264/35/35
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
