Triple
T973107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York City auxiliary police officers |
E20987
|
entity |
| Predicate | isArmedWithFirearm |
P6014
|
FINISHED |
| Object | false |
—
|
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: false | Statement: [New York City auxiliary police officers, isArmedWithFirearm, false]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isArmedWithFirearm Context triple: [New York City auxiliary police officers, isArmedWithFirearm, false]
-
A.
prohibitedWeaponType
Indicates that a particular type of weapon is classified as not allowed or forbidden under specified rules, laws, or agreements.
-
B.
gun
Indicates that one entity uses, carries, or is associated with a gun in relation to another entity or context.
-
C.
hasWeaponType
chosen
Indicates that an entity is associated with or equipped with a specific type or category of weapon.
-
D.
hasChamber
Indicates that one entity possesses, contains, or is associated with a distinct enclosed space or compartment (a chamber).
-
E.
gunType
Indicates the specific category or kind of gun associated with an 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b45f28f081908d41b2d7f353708d |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.