Triple
T10963283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Debbie Wasserman Schultz |
E259029
|
entity |
| Predicate | area of legislative activity |
P17365
|
FINISHED |
| Object | health care policy |
—
|
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: health care policy | Statement: [Debbie Wasserman Schultz, area of legislative activity, health care policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: area of legislative activity Context triple: [Debbie Wasserman Schultz, area of legislative activity, health care policy]
-
A.
areaOfLegislation
chosen
Indicates that one entity defines, concerns, or governs the legal domain or subject matter covered by another entity.
-
B.
legislativeActivity
Indicates involvement in creating, debating, amending, or passing laws or related legislative measures.
-
C.
legislativeImpact
Indicates the effect that a law or legislative action has on a policy, entity, or outcome.
-
D.
hasLegislativeSubject
Indicates that a legislative document, action, or body concerns, addresses, or is about a particular subject or topic.
-
E.
legislativeAudience
Indicates that the subject is the intended legislative body or group of lawmakers to whom a proposal, document, or action is directed.
- 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_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d77147f7108190a3b68ba1dd1c130e |
completed | April 9, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_69d72e8c27cc81908050590b7a04cafd |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.