Triple
T2588723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City Partnership |
E58064
|
entity |
| Predicate | hasAreaOfInfluence |
P2828
|
FINISHED |
| Object | New York City public 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: New York City public policy | Statement: [New York City Partnership, hasAreaOfInfluence, New York City public policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaOfInfluence Context triple: [New York City Partnership, hasAreaOfInfluence, New York City public policy]
-
A.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
B.
sphereOfInfluence
chosen
Indicates the area or domain within which an entity exerts significant control, impact, or authority over others.
-
C.
hasAreaOfInterest
Indicates that an entity possesses or is associated with a particular area of interest or focus.
-
D.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
E.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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_69ab4ac019c8819094add11c46706e32 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abd3fd1d608190a0cf0d12a9e6ce59 |
completed | March 7, 2026, 7:30 a.m. |
| PD | Predicate disambiguation | batch_69abd0d19308819089ee942513d567a4 |
completed | March 7, 2026, 7:16 a.m. |
Created at: March 6, 2026, 9:49 p.m.