Triple
T34277325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Luther |
E879494
|
entity |
| Predicate | hasPoliceBadgeType |
P1617
|
FINISHED |
| Object | Detective |
—
|
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: Detective | Statement: [John Luther, hasPoliceBadgeType, Detective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPoliceBadgeType Context triple: [John Luther, hasPoliceBadgeType, Detective]
-
A.
hasPoliceRole
Indicates that an entity holds or performs a specific role, duty, or function within a police or law enforcement context.
-
B.
hasPolicePartner
Indicates that one entity has another entity as its partner in a police or law-enforcement context.
-
C.
hasTypeOfInsignia
chosen
Indicates that an entity bears or is associated with a specific kind or category of insignia.
-
D.
hasBadgeNumber
Indicates that an entity is associated with a specific badge identification number.
-
E.
hasPoliceAI
Indicates that an entity is equipped with, governed by, or utilizes an artificial intelligence system specifically for policing or law-enforcement functions.
- 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff1c91bbac8190b84012dee1cb3b2c |
completed | May 9, 2026, 11:37 a.m. |
| PD | Predicate disambiguation | batch_69ff1c23ca508190bb5a435d765b7e53 |
completed | May 9, 2026, 11:36 a.m. |
Created at: May 1, 2026, 1:56 a.m.