Triple
T29097577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freddy Newandyke |
E735050
|
entity |
| Predicate | policeRole |
P152275
|
FINISHED |
| Object | infiltrates Joe Cabot's gang |
—
|
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: infiltrates Joe Cabot's gang | Statement: [Freddy Newandyke, policeRole, infiltrates Joe Cabot's gang]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policeRole Context triple: [Freddy Newandyke, policeRole, infiltrates Joe Cabot's gang]
-
A.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
B.
hasPoliceRole
chosen
Indicates that an entity holds or performs a specific role, duty, or function within a police or law enforcement context.
-
C.
lawEnforcementFunction
Indicates that an entity performs, is responsible for, or is associated with official law enforcement duties or activities.
-
D.
policeSystem
Indicates a relationship where an entity functions as, belongs to, or is governed by a system of law enforcement or policing.
-
E.
peakCivilianPolice
Indicates the highest recorded number of civilian police personnel present or deployed in a given context or operation.
- 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_69f05b0ed66481908f2e864fa550d2f1 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f6b49436b0819094e21603054d05d4 |
completed | May 3, 2026, 2:36 a.m. |
| PD | Predicate disambiguation | batch_69f6b3a5fd8481909433e923c5e24e55 |
completed | May 3, 2026, 2:32 a.m. |
Created at: April 28, 2026, 11:09 a.m.