Triple
T34496119
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Les Fugitifs |
E885607
|
entity |
| Predicate | hasPoliceCharacters |
P81289
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Les Fugitifs, hasPoliceCharacters, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPoliceCharacters Context triple: [Les Fugitifs, hasPoliceCharacters, yes]
-
A.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
B.
hasPoliceAI
Indicates that an entity is equipped with, governed by, or utilizes an artificial intelligence system specifically for policing or law-enforcement functions.
-
C.
hasPoliceTheme
chosen
Indicates that something features police, law enforcement, or policing activities as a central theme or focus.
-
D.
hasPoliceChief
Indicates that an entity has, is associated with, or is under the authority of a specific police chief.
-
E.
hasPolicePartner
Indicates that one entity has another entity as its partner in a police or law-enforcement context.
- 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_69f349cafcec8190997b45b3fdc16c27 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff246e0d4481908bcec718e1d4025b |
completed | May 9, 2026, 12:11 p.m. |
| PD | Predicate disambiguation | batch_69ff23cb70ac81909b776ace4597ae9c |
completed | May 9, 2026, 12:08 p.m. |
Created at: May 1, 2026, 2:01 a.m.