1993658fb2
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>
76 lines
4.1 KiB
Markdown
76 lines
4.1 KiB
Markdown
# SEC Fund Prospectus → RDF Triple Dataset — Design
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## Goal
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A training/eval dataset for text→RDF-triple extraction where:
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- the INPUT text is much larger than the OUTPUT JSON (realistic "needle in long doc"),
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- the OUTPUT is a genuine **graph of entity→entity relationships**, not flat key→literal attributes,
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- it is finance **reference data** (openfunds-aligned),
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- there is a **non-model gold baseline** AND a path to a **strong-model baseline**.
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## Why not flat XBRL fee data
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XBRL Risk/Return (management fee, TER, returns) yields only `Fund --pred--> literal`
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attributes — a star of literals, no entity-to-entity edges. Rejected as the *primary*
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graph source because it produces no relational structure. (May be added as optional
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literal-valued triples to enrich the schema, but it is not the point.)
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## Evidence (sample: Fidelity Oxford Street Trust, 485BPOS 0000028540-25-000048)
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Full prospectus prose = 1,061,532 chars. Relational mentions found in prose:
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adviser 381, sub-adviser 25, distributor 31, custodian 59, transfer agent 24,
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"Trust" 805, "series of" 13, "managed by" 38, named benchmark index.
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=> The prose contains a real multi-entity-type graph.
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## Target ontology (entity→entity edges)
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```
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Fund seriesOf Trust
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Fund advisedBy InvestmentAdviser
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Fund subAdvisedBy SubAdviser
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Fund distributedBy Distributor
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Fund custodian Custodian
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Fund transferAgent TransferAgent
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Fund managedBy PortfolioManager
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Fund tracksIndex Index (passive/index funds)
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```
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Optional literal-valued triples (attributes) to round out the openfunds record:
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managementFee, netExpenseRatio, return1yr/5yr/10yr, portfolioTurnover, objectiveText.
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## Baselines (the key selling point)
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1. GOLD, no model:
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- N-CEN service-provider table -> advisedBy, custodian, transferAgent, distributor, auditor (with LEIs)
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- Series/Class CSV -> seriesOf, hasShareClass (Trust->Series->Class skeleton)
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2. SILVER, strong model (GPT-4 / Opus), measured against the N-CEN gold:
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- managedBy (portfolio managers), subAdvisedBy, tracksIndex (named only in prose)
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## Unit of extraction
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One FUND (series) = one sample:
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input = the prospectus section(s) for that fund (kept within a long context)
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output = the fund's subgraph of triples
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N-CEN/Series-Class gold is keyed at trust/series level, so per-series is the natural join.
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## Data gaps to fix (local dump)
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- fund_data.db was accidentally deleted (only stale -shm/-wal remain) -> rebuild from data/.
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- load_xbrl_rr.py tag constants are partly stale vs the actual num.tsv vintage
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(e.g. ExpensesOverAssets, DistributionAndService12b1FeesOverAssets,
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AvgAnnlRtrPct/AnnlRtrPct, PortfolioTurnoverRate, BarChart*QuarterlyReturn).
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- Local N-PORT dump is MISSING FUND_REPORTED_HOLDING.tsv and IDENTIFIERS.tsv
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(the holds/issuedBy edges) -> would need re-download if holdings edges are wanted.
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- XBRL flat files contain NO text blocks -> narrative input must be fetched from EDGAR
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filings (filing_text), not from the flat files.
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- N-CEN flat files are NOT yet downloaded locally -> needed for the gold edges.
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## Holdings edges (deferred to a 2nd track)
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Holdings (`fund holds security`, `security issuedBy issuer`) are NOT in the prospectus.
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Text-bearing sources for holdings:
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- Annual/Semi-Annual Report (N-CSR): "Management Discussion" (MDFP) names TOP holdings
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in prose -> real `holds` edges; gold = N-PORT holdings table. The full Schedule of
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Investments is a table, not prose.
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- Fund fact sheets / PM commentary (fund-company marketing): "Top 10 Holdings" + prose,
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but off-EDGAR and no standardized machine-readable gold.
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Plan: build prospectus->service-provider graph FIRST (clean N-CEN gold). Add an
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MDFP(N-CSR)->holdings dataset as a SECOND finance sub-domain later — this also
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strengthens the thesis's cross-domain generalization claim.
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## This session: PROOF OF CONCEPT (~20-50 funds)
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End-to-end on a small slice: fetch prospectus text, get N-CEN gold service-provider
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edges + Series/Class structure, emit text->triples samples in the <triple_start>
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RDF marker format, and a gold-baseline scorer. Validate before scaling.
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