Triple
T26453180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yonkers Police Department |
E665410
|
entity |
| Predicate | usesForcePolicy |
P11330
|
FINISHED |
| Object | use-of-force policy of Yonkers Police Department |
—
|
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: use-of-force policy of Yonkers Police Department | Statement: [Yonkers Police Department, usesForcePolicy, use-of-force policy of Yonkers Police Department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesForcePolicy Context triple: [Yonkers Police Department, usesForcePolicy, use-of-force policy of Yonkers Police Department]
-
A.
usedPolicy
Indicates that an entity applied or followed a particular policy in carrying out an action or decision.
-
B.
usesForces
Indicates that one entity applies physical, magical, or other types of forces to influence, move, or affect another entity.
-
C.
usesPolicyModel
chosen
Indicates that one entity applies, relies on, or operates according to a particular policy model.
-
D.
inForceWith
Indicates that a rule, condition, or agreement is currently active and being applied in conjunction with a specified entity or set of entities.
-
E.
usesPolicyRate
Indicates that one entity applies or bases its actions or decisions on a specified policy interest rate.
- 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_69ee883d5040819097dd154643005230 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f61f12b0f08190bc4a16907941864c |
completed | May 2, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69f61b3a8ae0819090189fbd8eb19f2f |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 27, 2026, 12:07 a.m.