fund_rfid_data/data/RDF_DATASET_DESIGN.md

SEC Fund Prospectus → RDF Triple Dataset — Design

Goal

A training/eval dataset for text→RDF-triple extraction where:

• the INPUT text is much larger than the OUTPUT JSON (realistic "needle in long doc"),
• the OUTPUT is a genuine graph of entity→entity relationships, not flat key→literal attributes,
• it is finance reference data (openfunds-aligned),
• there is a non-model gold baseline AND a path to a strong-model baseline.

Why not flat XBRL fee data

XBRL Risk/Return (management fee, TER, returns) yields only Fund --pred--> literal attributes — a star of literals, no entity-to-entity edges. Rejected as the primary graph source because it produces no relational structure. (May be added as optional literal-valued triples to enrich the schema, but it is not the point.)

Evidence (sample: Fidelity Oxford Street Trust, 485BPOS 0000028540-25-000048)

Full prospectus prose = 1,061,532 chars. Relational mentions found in prose: adviser 381, sub-adviser 25, distributor 31, custodian 59, transfer agent 24, "Trust" 805, "series of" 13, "managed by" 38, named benchmark index. => The prose contains a real multi-entity-type graph.

Target ontology (entity→entity edges)

Fund      seriesOf       Trust
Fund      advisedBy      InvestmentAdviser
Fund      subAdvisedBy   SubAdviser
Fund      distributedBy  Distributor
Fund      custodian      Custodian
Fund      transferAgent  TransferAgent
Fund      managedBy      PortfolioManager
Fund      tracksIndex    Index            (passive/index funds)

Optional literal-valued triples (attributes) to round out the openfunds record: managementFee, netExpenseRatio, return1yr/5yr/10yr, portfolioTurnover, objectiveText.

Baselines (the key selling point)

1. GOLD, no model:
   • N-CEN service-provider table -> advisedBy, custodian, transferAgent, distributor, auditor (with LEIs)
   • Series/Class CSV -> seriesOf, hasShareClass (Trust->Series->Class skeleton)
2. SILVER, strong model (GPT-4 / Opus), measured against the N-CEN gold:
   • managedBy (portfolio managers), subAdvisedBy, tracksIndex (named only in prose)

Unit of extraction

One FUND (series) = one sample: input = the prospectus section(s) for that fund (kept within a long context) output = the fund's subgraph of triples N-CEN/Series-Class gold is keyed at trust/series level, so per-series is the natural join.

Data gaps to fix (local dump)

• fund_data.db was accidentally deleted (only stale -shm/-wal remain) -> rebuild from data/.
• load_xbrl_rr.py tag constants are partly stale vs the actual num.tsv vintage (e.g. ExpensesOverAssets, DistributionAndService12b1FeesOverAssets, AvgAnnlRtrPct/AnnlRtrPct, PortfolioTurnoverRate, BarChart*QuarterlyReturn).
• Local N-PORT dump is MISSING FUND_REPORTED_HOLDING.tsv and IDENTIFIERS.tsv (the holds/issuedBy edges) -> would need re-download if holdings edges are wanted.
• XBRL flat files contain NO text blocks -> narrative input must be fetched from EDGAR filings (filing_text), not from the flat files.
• N-CEN flat files are NOT yet downloaded locally -> needed for the gold edges.

Holdings edges (deferred to a 2nd track)

Holdings (fund holds security, security issuedBy issuer) are NOT in the prospectus. Text-bearing sources for holdings:

• Annual/Semi-Annual Report (N-CSR): "Management Discussion" (MDFP) names TOP holdings in prose -> real holds edges; gold = N-PORT holdings table. The full Schedule of Investments is a table, not prose.
• Fund fact sheets / PM commentary (fund-company marketing): "Top 10 Holdings" + prose, but off-EDGAR and no standardized machine-readable gold. Plan: build prospectus->service-provider graph FIRST (clean N-CEN gold). Add an MDFP(N-CSR)->holdings dataset as a SECOND finance sub-domain later — this also strengthens the thesis's cross-domain generalization claim.

This session: PROOF OF CONCEPT (~20-50 funds)

End-to-end on a small slice: fetch prospectus text, get N-CEN gold service-provider edges + Series/Class structure, emit text->triples samples in the <triple_start> RDF marker format, and a gold-baseline scorer. Validate before scaling.