Triple
T1016630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Transportation Bureau (NYPD) |
E21943
|
entity |
| Predicate | typeOfLawEnforcementActivity |
P7908
|
FINISHED |
| Object | traffic law enforcement |
—
|
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: traffic law enforcement | Statement: [Transportation Bureau (NYPD), typeOfLawEnforcementActivity, traffic law enforcement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfLawEnforcementActivity Context triple: [Transportation Bureau (NYPD), typeOfLawEnforcementActivity, traffic law enforcement]
-
A.
typeOfLawEnforcement
chosen
Indicates that one entity is a specific kind or category of law enforcement associated with another entity.
-
B.
legalAct
Indicates that an entity performs, enacts, or is involved in a formal legal action, measure, or proceeding under a legal framework.
-
C.
typeOfLaw
Indicates that one entity is a specific category or kind of law to which the other entity pertains.
-
D.
lawEnforcementResponse
Indicates the actions or measures taken by law enforcement agencies in reaction to an incident, behavior, or situation.
-
E.
enforcedLaw
Indicates that an authority actively applies or upholds a specific law to regulate behavior or resolve situations.
- 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_69a493c68e24819080ed0ee8bcfd5ce0 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b7c1e9d08190baf7e81f3777168d |
completed | March 1, 2026, 10:03 p.m. |
| PD | Predicate disambiguation | batch_69a4b7238d4c8190b22d6c2ac0ac4911 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:41 p.m.