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>
47 lines
918 B
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47 lines
918 B
Plaintext
# ---- Large / derived data (reproducible via build_rdf_dataset.py) ----
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# Raw prospectus prose fetched from EDGAR (GBs)
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data/rdf_poc/prose/
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# Generated training samples and splits (embed raw SEC text, 100s of MB)
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data/rdf_poc/samples.jsonl
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data/rdf_poc/samples_full.jsonl
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data/rdf_poc/train.jsonl
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data/rdf_poc/val.jsonl
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data/rdf_poc/test.jsonl
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# LLM match inputs/outputs (embed raw text / large)
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data/rdf_poc/match_input.jsonl
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data/rdf_poc/match_all.jsonl
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# diagnostic / scratch files
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data/rdf_poc/_*.jsonl
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# prose backups from iterative fetch-strategy changes
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data/rdf_poc/prose_*_bak/
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# Raw SEC bulk downloads (re-downloadable from sec.gov)
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data/ncen/
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data/nport/
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data/xbrl_rr/
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# SQLite working DB
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fund_data.db
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fund_data.db-shm
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fund_data.db-wal
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# Archives
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*.zip
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# Python
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__pycache__/
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*.pyc
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*.pyo
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# LaTeX build artifacts
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*.aux
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*.log
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*.out
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*.toc
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*.fls
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*.fdb_latexmk
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*.synctex.gz
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# OS / editor
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.DS_Store
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.claude/
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