Drop non-extractable custodian relation; add per-triple grounded flag
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>
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Florian Herzog
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README.md
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README.md
@ -54,19 +54,33 @@ Each line of `samples.jsonl` / `train|val|test.jsonl` is a JSON record:
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|---|---|
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| `input_text` | prospectus prose for the fund (model input) |
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| `ontology` | inferred meta-schema (subject type → predicate → object type) |
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| `target_triples` | structured `{s,p,o}` list |
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| `target_triples` | structured `{s,p,o,grounded}` list (`grounded` = object name appears in `input_text`) |
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| `target_serialized` | marker form (`<triple_start>` …) for Models 2/4 |
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| `target_serialized_plain` | Turtle-like form, no special tokens, for Models 1/3 |
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| `cik`, `series_id`, `fund`, `trust_name` | identifiers |
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| `stats` | input/target sizes, triple count, text:json ratio |
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| `stats` | input/target sizes, triple count, `n_grounded`, text:json ratio |
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## Relations
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Entity-to-entity edges (gold from N-CEN / Series-Class):
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`seriesOf`, `advisedBy`, `subAdvisedBy`, `transferAgent`, `custodian` (primary
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only by default), `administrator`, `underwrittenBy`. Holdings edges
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(`holds`/`issuedBy`/`domiciledIn`, gold from N-PORT) are a planned second track
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from annual-report (N-CSR) commentary — see the description PDF.
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`seriesOf`, `advisedBy`, `subAdvisedBy`, `transferAgent`, `administrator`,
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`underwrittenBy`.
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**`custodian` is dropped by default** (`--custodian-scope none`): custodian names
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— especially foreign sub-custodians — appear only in the structured N-CEN table
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and in *no* prose document (the summary prospectus says only "the custodian"),
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so they are not extractable from text. The primary custodian is named only in the
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separately-filed SAI (N-1A Part B), which is not part of the input. Use
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`--custodian-scope primary` or `all` to re-include it if you add the SAI as input.
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**Prose-grounding:** every triple carries a `grounded` flag (object name present
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in the sample's input). Across the full build ~80 % of triples are grounded
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(per relation: advisedBy 93 %, seriesOf/subAdvisedBy/administrator 80–84 %,
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transferAgent 72 %, underwrittenBy 62 %). Filter on `grounded` to train/evaluate
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only on text-extractable targets.
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Holdings edges (`holds`/`issuedBy`/`domiciledIn`, gold from N-PORT) are a planned
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second track from annual-report (N-CSR) commentary — see the description PDF.
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## Data sources
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@ -394,6 +394,39 @@ def serialize_triples_plain(triples, entities) -> str:
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return "\n".join(chunks)
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_GND_SUFFIX = re.compile(
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r"\b(llc|l\.l\.c|inc|incorporated|corp|corporation|company|co|ltd|limited"
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r"|lp|l\.p|llp|n\.a|na|the|trust)\b", re.I)
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def _gnorm(s: str) -> str:
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"""Normalize a name for prose-grounding checks (lowercase, strip legal suffixes)."""
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s = _GND_SUFFIX.sub(" ", (s or "").lower())
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s = re.sub(r"[^a-z0-9]+", " ", s)
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return re.sub(r"\s+", " ", s).strip()
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def annotate_grounding(triples, entities, text):
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"""Mark each triple as prose-grounded if its object name appears in `text`.
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A triple is only useful as a TEXT->triple training target if the fact can be
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found in the input. This adds a boolean `grounded` to each triple (the
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normalized object label occurs as a substring of the normalized input), so
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training/eval can restrict to grounded triples. Returns (annotated_triples,
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n_grounded). Mutates copies, not the input dicts.
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"""
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ntext = _gnorm(text)
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out = []
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n_grounded = 0
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for t in triples:
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olbl = entities.get(t["o"], {}).get("label", "")
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no = _gnorm(olbl)
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g = bool(no) and no in ntext
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n_grounded += int(g)
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out.append({**t, "grounded": g})
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return out, n_grounded
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def ontology_schema(triples, entities) -> dict:
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"""Inferred meta-schema (subject type -> predicate -> object type), per thesis 5.3."""
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schema = defaultdict(lambda: defaultdict(set))
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@ -580,6 +613,7 @@ def _build_samples_per_fund():
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if not segs: # whole-trust fallback (no section located)
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n_fallback_trusts += 1
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triples = g["triples"]
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triples, n_gnd = annotate_grounding(triples, ents, text)
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target = serialize_triples(triples, ents)
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target_plain = serialize_triples_plain(triples, ents)
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rec = {
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@ -590,7 +624,8 @@ def _build_samples_per_fund():
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"target_serialized": target,
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"target_serialized_plain": target_plain,
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"stats": {"input_chars": len(text), "target_chars": len(target),
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"n_triples": len(triples), "n_entities": len(ents),
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"n_triples": len(triples), "n_grounded": n_gnd,
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"n_entities": len(ents),
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"text_to_json_ratio": round(len(text) / max(1, len(target)), 1)},
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}
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out.write(json.dumps(rec, ensure_ascii=False) + "\n")
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@ -611,6 +646,7 @@ def _build_samples_per_fund():
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for t in triples:
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ref.add(t["s"]); ref.add(t["o"])
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sub_ents = {k: ents[k] for k in ref if k in ents}
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triples, n_gnd = annotate_grounding(triples, sub_ents, seg)
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target = serialize_triples(triples, sub_ents)
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target_plain = serialize_triples_plain(triples, sub_ents)
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rec = {
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@ -623,7 +659,8 @@ def _build_samples_per_fund():
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"target_serialized": target,
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"target_serialized_plain": target_plain,
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"stats": {"input_chars": len(seg), "target_chars": len(target),
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"n_triples": len(triples), "n_entities": len(sub_ents),
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"n_triples": len(triples), "n_grounded": n_gnd,
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"n_entities": len(sub_ents),
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"text_to_json_ratio": round(len(seg) / max(1, len(target)), 1)},
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}
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out.write(json.dumps(rec, ensure_ascii=False) + "\n")
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File diff suppressed because one or more lines are too long
Binary file not shown.
@ -189,11 +189,14 @@ Fund & \code{seriesOf} & Trust \\
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Fund & \code{advisedBy} & InvestmentAdviser \\
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Fund & \code{subAdvisedBy} & SubAdviser \\
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Fund & \code{transferAgent} & TransferAgent \\
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Fund & \code{custodian} & Custodian \\
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Fund & \code{administrator} & Administrator \\
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Trust & \code{underwrittenBy} & Distributor \\
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\addlinespace
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Fund & \code{holds} & Security \quad(holdings sub-graph) \\
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\multicolumn{3}{@{}l}{\emph{dropped (not prose-grounded, see \S\ref{sec:baselines}):}}\\
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Fund & \code{custodian} & Custodian \\
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\addlinespace
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\multicolumn{3}{@{}l}{\emph{holdings sub-graph (planned 2nd track):}}\\
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Fund & \code{holds} & Security \\
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Security & \code{issuedBy} & Issuer \\
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Security & \code{domiciledIn} & Country \\
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Fund & \code{tracksIndex} & Index \\
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@ -220,7 +223,6 @@ realised in that sample, e.g.
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"advisedBy": ["InvestmentAdviser"],
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"subAdvisedBy": ["SubAdviser"],
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"transferAgent": ["TransferAgent"],
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"custodian": ["Custodian"],
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"administrator": ["Administrator"]
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},
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"Trust": { "underwrittenBy": ["Distributor"] }
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@ -242,7 +244,7 @@ realised in that sample, e.g.
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\node[ent,above=of trust] (dist) {Distributor};
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\node[ent,left=of fund] (adv) {Investment\\Adviser};
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\node[ent,below=16mm of adv] (sub) {Sub-\\Adviser};
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\node[ent,below=of fund] (cust) {Custodian};
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\node[ent,dashed,fill=black!5,below=of fund] (cust) {Custodian};
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\node[ent,right=24mm of cust] (ta) {Transfer\\Agent};
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\node[ent,right=of fund] (admin) {Administrator};
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% --- holdings cluster (far right, separated column) ---
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@ -254,7 +256,7 @@ realised in that sample, e.g.
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\draw[edge] (trust) -- node[lbl]{underwrittenBy} (dist);
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\draw[edge] (fund) -- node[lbl]{advisedBy} (adv);
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\draw[edge] (fund) -- node[lbl,pos=0.55]{subAdvisedBy} (sub);
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\draw[edge] (fund) -- node[lbl]{custodian} (cust);
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\draw[edge,dashed,gray] (fund) -- node[lbl]{custodian} (cust);
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\draw[edge] (fund) -- node[lbl,pos=0.55]{transferAgent} (ta);
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\draw[edge] (fund) -- node[lbl]{administrator} (admin);
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% holds: arc from the Fund's top, over the Administrator, down to Security
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@ -264,9 +266,10 @@ realised in that sample, e.g.
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\draw[edge] (sec) -- node[lbl]{domiciledIn} (ctry);
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\end{tikzpicture}
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\caption{Schematic of the target knowledge graph. Left and centre: the
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service-provider/structure graph grounded in the prospectus prose. Right column
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(Issuer--Security--Country): the holdings sub-graph grounded in annual-report
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commentary with N-PORT gold.}
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service-provider/structure graph grounded in the prospectus prose. The dashed
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\code{custodian} edge is \emph{dropped} from the dataset (not prose-grounded,
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\S\ref{sec:baselines}). Right column (Issuer--Security--Country): the holdings
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sub-graph grounded in annual-report commentary with N-PORT gold.}
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\label{fig:graph}
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\end{figure}
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@ -322,18 +325,22 @@ thesis (Section~5.2), in which four special tokens delimit triple components and
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shared subjects/predicates are factored out, mirroring Turtle's predicate-object
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lists:
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\begin{lstlisting}[language=,caption={Target serialization for one segmented fund (primary-custodian scope).},captionpos=b]
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<triple_start> Small Cap Special Values Fund
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\begin{lstlisting}[language=,caption={Target serialization for one segmented fund (custodian dropped).},captionpos=b]
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<triple_start> John Hancock Bond Fund
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<predicate_marker> seriesOf
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<object_marker> VALIC Co I
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<object_marker> JOHN HANCOCK SOVEREIGN BOND FUND
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<predicate_marker> advisedBy
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<object_marker> The Variable Annuity Life Insurance Company
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<object_marker> John Hancock Investment Management LLC
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<predicate_marker> subAdvisedBy
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<object_marker> SunAmerica Asset Management, LLC
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<object_marker> Manulife Investment Management (US) LLC
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<predicate_marker> administrator
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<object_marker> SunAmerica Asset Management, LLC
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<predicate_marker> custodian
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<object_marker> State Street Bank and Trust Company
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<object_marker> John Hancock Investment Management LLC
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<predicate_marker> transferAgent
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<object_marker> John Hancock Signature Services, Inc.
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<triple_end>
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<triple_start> JOHN HANCOCK SOVEREIGN BOND FUND
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<predicate_marker> underwrittenBy
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<object_marker> JOHN HANCOCK INVESTMENT MANAGEMENT DISTRIBUTORS LLC
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<triple_end>
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\end{lstlisting}
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@ -348,8 +355,9 @@ question~1.
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Each sample is thus a JSON record with: the input prose (\code{input\_text}), the
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inferred ontology (\code{ontology}), the target triples as a structured list
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(\code{target\_triples}) and in both serializations, the trust/series identifiers,
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and size statistics.
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(\code{target\_triples}, each triple carrying a \code{grounded} flag) and in both
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serializations, the trust/series identifiers, and size statistics (including the
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grounded-triple count).
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% ====================================================================
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\section{Per-fund segmentation}
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@ -417,70 +425,64 @@ fetching more filings of the same CIK, and is left to the full-scale build.
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The dataset offers two independent ground-truth regimes.
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\paragraph{Model-free gold.} For \code{advisedBy}, \code{subAdvisedBy},
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\code{transferAgent}, \code{custodian}, \code{administrator} and
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\code{underwrittenBy}, the labels come directly from N-CEN; for \code{seriesOf}
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and \code{hasShareClass}, from the Series/Class reference data; for \code{holds},
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\code{issuedBy} and \code{domiciledIn}, from N-PORT. No model is involved in
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producing these labels, which makes them an unusually trustworthy reference for a
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generative-extraction benchmark.
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\code{transferAgent}, \code{administrator} and \code{underwrittenBy}, the labels
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come directly from N-CEN; for \code{seriesOf}, from the Series/Class reference
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data; for the (planned) \code{holds}, \code{issuedBy} and \code{domiciledIn}, from
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N-PORT. No model is involved in producing these labels, which makes them an
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unusually trustworthy reference for a generative-extraction benchmark.
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\paragraph{The custodian relation and edge scoping.} The custodian relation
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illustrates a subtlety that any honest benchmark on this data must address. N-CEN
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reports, for a global fund, not only its \emph{primary} custodian but the entire
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chain of \emph{foreign sub-custodians} --- one bank per market it invests in.
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These sub-custodians have two damaging properties. First, they are
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\emph{unextractable}: they appear only in the N-CEN table and essentially never in
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the prospectus prose (a naive string-match recovers $7\%$ of them), so keeping
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them as targets asks the model to extract facts absent from its input. Second,
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they \emph{dominate}: with \code{IS\_SUB\_CUSTODIAN${=}$Y} accounting for $88\%$
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of custodian rows, they constitute roughly two thirds of \emph{all} edges in the
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unfiltered graph, inflating both the target size and the training loss with noise.
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The dataset therefore scopes the custodian relation to the \emph{primary}
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custodian (\code{IS\_SUB\_CUSTODIAN${\neq}$Y}, a median of one per fund), which is
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genuinely prose-grounded --- it is named in the prospectus or its Statement of
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Additional Information (e.g.\ ``State Street Bank and Trust Company serves as
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custodian''). This single change reduces the corpus from $36{,}880$ to $15{,}739$
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edges, all of prose-grounded relation types, and is the configurable default
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(\code{--custodian-scope primary}). The full sub-custodian chain remains available
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in N-CEN as a structured-only relation outside the text-to-triples task. This is a
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dataset-quality decision of the same kind the thesis notes for T-REx and REBEL,
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whose non-exhaustive references unfairly penalise correct extractions.
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\paragraph{Why the custodian relation is dropped.} A text-to-triple target is only
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useful if the fact can be found in the input text. The custodian relation fails
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this test and is therefore \emph{excluded} from the dataset
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(\code{--custodian-scope none}). N-CEN reports, for a global fund, the entire chain
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of \emph{foreign sub-custodians} --- one bank per market it invests in (Citibank
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Brazil, Banco de Chile, Cititrust Colombia, \dots). These names appear only in the
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structured N-CEN table and in \emph{no} prose document: the summary prospectus
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refers to the custodian only generically (``\dots\ including the adviser, the
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custodian, and the transfer agent\dots'') and never names it. Even the
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\emph{primary} custodian is typically named only in the separately filed Statement
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of Additional Information (N-1A Part~B), which is not part of the fetched input.
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Measured per fund, the custodian object name occurs in the fund's own prospectus
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segment only $28\%$ of the time --- by far the weakest of all relations --- so
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keeping it would systematically ask the model to extract facts absent from its
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input. The full custodian chain remains available in N-CEN as a structured-only
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relation, outside the text-to-triples task; recovering it from text would require
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adding the SAI as an input source (a separate crawl).
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\paragraph{No-model lower bound.} A trivial string-matching baseline --- emit a
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gold edge iff the object's name occurs in the prose --- establishes a \emph{floor}
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and measures \emph{how prose-grounded each relation is}. Table~\ref{tab:baseline}
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reports this on the proof-of-concept slice after primary-custodian scoping,
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multi-book fetching and per-fund segmentation. Because the baseline requires an
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\emph{exact substring} match within the fund's \emph{own} section, its recall is a
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strict lower bound: a fund's adviser, for instance, must be named in that fund's
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segment under a literal spelling. On the full quarter the adviser is recovered
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with recall $0.93$ and the micro-averaged $F_1$ reaches $0.79$; the recovered
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custodian recall is $0.63$ (up from $0.07$ unscoped and $0.37$ after scoping
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alone). The residual gap from $1.0$ is attributable to surface-form variation
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(``State Street Bank and Trust Company'' vs.\ ``State Street'') that a trained
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model handles but exact matching does not.
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\paragraph{Per-triple grounding flag.} Because even the retained relations are not
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\emph{always} named in a fund's segment, every triple carries a boolean
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\code{grounded} flag: true iff the normalized object name occurs in that sample's
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input text. This lets training and evaluation restrict to grounded triples rather
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than silently carrying unextractable targets. Across the full build, $80\%$ of
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triples are grounded; per relation the rate ranges from $93\%$ (\code{advisedBy})
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down to $62\%$ (\code{underwrittenBy}), as shown in Table~\ref{tab:baseline}.
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\paragraph{No-model lower bound.} The grounding flag is itself a trivial,
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model-free baseline (emit a gold edge iff its object name occurs in the prose);
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its per-relation rate is a strict lower bound on recall, since it requires an
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\emph{exact substring} match within the fund's own segment and so misses surface
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variants (``State Street Bank and Trust Company'' vs.\ ``State Street'') that a
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trained model handles. Table~\ref{tab:baseline} reports it on the full build.
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\begin{table}[h]
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\centering
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\caption{No-model string-match baseline on the full 2025\,Q3 build, after
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primary-custodian scoping, multi-book fetching and per-fund segmentation
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($852$ samples). Precision is $1.00$ by construction; recall is a strict
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exact-match lower bound.}
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\caption{Per-relation prose-grounding on the full 2025\,Q3 build ($852$ samples,
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$8{,}824$ triples; custodian dropped). ``Grounded'' = object name present in the
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sample's input; a strict, model-free lower bound on recall.}
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\label{tab:baseline}
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\small
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\begin{tabular}{@{}lrr@{}}
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\toprule
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Relation & Recall & Gold edges \\
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Relation & Triples & Grounded \\
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\midrule
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\code{advisedBy} & 0.93 & 1{,}673 \\
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\code{seriesOf} & 0.84 & 1{,}555 \\
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\code{subAdvisedBy} & 0.84 & 946 \\
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\code{administrator} & 0.80 & 2{,}066 \\
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\code{transferAgent} & 0.72 & 1{,}721 \\
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\code{custodian} & 0.63 & 1{,}761 \\
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\code{underwrittenBy} & 0.62 & 863 \\
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\code{advisedBy} & 1{,}673 & 93\% \\
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\code{seriesOf} & 1{,}555 & 84\% \\
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\code{subAdvisedBy} & 946 & 84\% \\
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\code{administrator} & 2{,}066 & 80\% \\
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\code{transferAgent} & 1{,}721 & 72\% \\
|
||||
\code{underwrittenBy} & 863 & 62\% \\
|
||||
\midrule
|
||||
micro-average & 0.65 & 6{,}479 \\
|
||||
all & 8{,}824 & 80\% \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
\end{table}
|
||||
@ -497,21 +499,22 @@ quantified rather than assumed.
|
||||
\section{Corpus statistics}
|
||||
% ====================================================================
|
||||
|
||||
Table~\ref{tab:stats} summarises one full quarter (2025\,Q3) of the dataset. The
|
||||
N-CEN gold graph (after primary-custodian scoping) holds $15{,}739$
|
||||
Table~\ref{tab:stats} summarises one full quarter (2025\,Q3) of the dataset. With
|
||||
the custodian relation dropped, the N-CEN gold graph holds $12{,}694$
|
||||
entity-to-entity edges across $435$ trusts and $2{,}421$ funds. Fetching all full
|
||||
prospectus books for every trust ($2{,}326$ filings across $393$ trusts; $42$
|
||||
closed-end or interval funds file no standard prospectus) and applying the robust
|
||||
per-fund segmenter yields $852$ samples ($659$ cleanly segmented per-fund plus
|
||||
$193$ whole-trust fallbacks). The segmented samples have a per-fund median ratio
|
||||
near $117\!:\!1$ (input prose to target serialization), and across all samples the
|
||||
$193$ whole-trust fallbacks) containing $8{,}824$ target triples, of which $80\%$
|
||||
are prose-grounded. The segmented samples have a per-fund median ratio near
|
||||
$117\!:\!1$ (input prose to target serialization), and across all samples the
|
||||
median exceeds $400\!:\!1$ --- the inverse of the symmetric-size benchmarks.
|
||||
|
||||
\begin{table}[h]
|
||||
\centering
|
||||
\caption{Corpus statistics for the full 2025\,Q3 build. Left: N-CEN gold graph
|
||||
(primary-custodian scope). Right: text-to-triple samples (all prospectus books per
|
||||
trust, per-fund segmentation).}
|
||||
\caption{Corpus statistics for the full 2025\,Q3 build (custodian dropped). Left:
|
||||
N-CEN gold graph. Right: text-to-triple samples (all prospectus books per trust,
|
||||
per-fund segmentation).}
|
||||
\label{tab:stats}
|
||||
\small
|
||||
\begin{tabular}{@{}lr@{\hskip 3em}lr@{}}
|
||||
@ -520,9 +523,9 @@ trust, per-fund segmentation).}
|
||||
\cmidrule(r){1-2}\cmidrule(l){3-4}
|
||||
Trust graphs & 435 & Samples (total) & 852 \\
|
||||
Funds (series) & 2{,}421 & \;segmented per-fund & 659 \\
|
||||
Entity-entity edges & 15{,}739 & \;whole-trust fallback & 193 \\
|
||||
\;custodian (primary) & 3{,}045 & Trusts fetched & 393 \\
|
||||
\;advisedBy & 2{,}588 & Prospectus filings & 2{,}326 \\
|
||||
Entity-entity edges & 12{,}694 & \;whole-trust fallback & 193 \\
|
||||
\;administrator & 3{,}288 & Target triples & 8{,}824 \\
|
||||
\;advisedBy & 2{,}588 & \;grounded & 80\% \\
|
||||
Distributors & 458 & Ratio (median, per-fund) & $117\!:\!1$ \\
|
||||
\bottomrule
|
||||
\end{tabular}
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user