Ensures text<->gold agreement for the text->triple dataset:
- fetch: newest full prospectus book + later 497 supplements (time-aligned with
the current N-CEN gold; fixes stale 'largest book' picking 2-5yr-old filings)
- grounding: fast alias matcher (name present, variant-tolerant) AND an LLM
role-check (llm_extract.py match mode, via local Ollama or remote vLLM server)
that verifies the entity plays that ROLE -- catches right-name/wrong-role cases
a lexical matcher over-keeps. Validated with a strong model; ~93% of gold names
are present once granularity+currency are fixed.
- llm_extract.py: extract (baseline) + match (grounding) modes, sliding-window
with retrieval pre-filter, claim dedup, retry, ollama/vllm backends
- build_rdf_dataset.py: --grounding alias|llm|name|context|none, whole-trust
samples now filtered, MIN_PROSE stub guard
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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