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>
Custodian names (esp. foreign sub-custodians) appear only in structured N-CEN,
never in the prospectus prose, so they are not a valid text->triple target.
Per-fund the custodian object name occurs in only 28% of segments, the weakest
of all relations. Default is now --custodian-scope none.
Every triple now carries a 'grounded' boolean (object name present in the
sample's input text); 80% of triples are grounded across the full build. This
lets training/eval restrict to text-extractable targets.
- build_rdf_dataset.py: annotate_grounding() + grounded flag in samples/stats
- gold rebuilt without custodian (15,739 -> 12,694 edges)
- dataset_description + README updated (custodian dropped, grounding documented)
Reported by thesis author: Citibank custodians in triples for 0001529390 never
appear in that prospectus text.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Builds a relationship-rich finance dataset for text-to-RDF-triple extraction
from SEC fund disclosures, the dataset for the thesis 'Magical RDF Triples and
how to synthetize them'.
- build_rdf_dataset.py: gold (N-CEN graphs), fetch (EDGAR prospectus prose,
all books per trust), samples (per-fund segmentation, marker + plain
serializations), split (trust-level 80/10/10, no leakage)
- score_baseline.py: no-model string-match baseline + strong-model scorer
- dataset_description.{tex,pdf}: scientific description of the dataset
- data/rdf_poc/gold_graphs.jsonl: structured gold knowledge graph (2025Q3)
- Large prose/sample files and raw SEC downloads are gitignored (reproducible)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
