Triple
T7494970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Edith Nourse Rogers |
E177098
|
entity |
| Predicate | fieldOfLegislativeFocus |
P1876
|
FINISHED |
| Object | veterans’ benefits |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: veterans’ benefits | Statement: [Edith Nourse Rogers, fieldOfLegislativeFocus, veterans’ benefits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOfLegislativeFocus Context triple: [Edith Nourse Rogers, fieldOfLegislativeFocus, veterans’ benefits]
-
A.
hasLegislativeSubject
Indicates that a legislative document, action, or body concerns, addresses, or is about a particular subject or topic.
-
B.
policyFocus
chosen
Indicates that an entity (such as a person, organization, or document) is primarily concerned with, directed toward, or centered on a particular policy area or issue.
-
C.
areaOfLegislation
Indicates that one entity defines, concerns, or governs the legal domain or subject matter covered by another entity.
-
D.
relatedLegislation
Indicates that there exists a legislative document that is connected to, affects, or is otherwise relevant to the subject entity.
-
E.
legislativeActivity
Indicates involvement in creating, debating, amending, or passing laws or related legislative measures.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c69f2583808190bd1a4936c42a5815 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f81b431481908214b69c6c8d83bc |
completed | March 27, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d266d88190982cf5d2ee2e9564 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:43 p.m.