Triple
T7158563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senate Special Committee on Aging |
E166877
|
entity |
| Predicate | hasConstituencyFocus |
P22245
|
FINISHED |
| Object | older adults in the United States |
—
|
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: older adults in the United States | Statement: [Senate Special Committee on Aging, hasConstituencyFocus, older adults in the United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasConstituencyFocus Context triple: [Senate Special Committee on Aging, hasConstituencyFocus, older adults in the United States]
-
A.
containsConstituency
Indicates that one entity includes or encompasses another entity as part of its constituency or membership.
-
B.
coreConstituency
Indicates that one entity serves as the primary, most important group of supporters or audience for another entity.
-
C.
targetConstituency
chosen
Indicates that something is directed toward, intended for, or affects a particular constituency or group of stakeholders.
-
D.
hasNumberOfConstituencies
Indicates the specific count of constituencies associated with an entity.
-
E.
constituency
Indicates that one entity functions as a constituent or component part within the structural or organizational makeup of another entity.
- 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_69c68887a5cc8190bec0ea96227164f7 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e8109a64819087e132d6d483c07d |
completed | March 27, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69c6e1cd5c948190a9113b23f7308c21 |
completed | March 27, 2026, 8 p.m. |
Created at: March 27, 2026, 2:47 p.m.