Triple
T10719844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sparta, Mississippi |
E252786
|
entity |
| Predicate | hasFictionalLawEnforcementRole |
P31758
|
FINISHED |
| Object | chief of police |
—
|
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: chief of police | Statement: [Sparta, Mississippi, hasFictionalLawEnforcementRole, chief of police]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFictionalLawEnforcementRole Context triple: [Sparta, Mississippi, hasFictionalLawEnforcementRole, chief of police]
-
A.
hasFictionalRole
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
B.
policeCharacter
chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
C.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
D.
hasInUniverseRole
Indicates that an entity holds or performs a specific role or function within a particular fictional or defined universe.
-
E.
hasFictionalStaffMember
Indicates that an entity includes or employs a staff member who is a fictional character.
- 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_69d6aa5d8be481909a43218b2bfdbe95 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6ff3722ec8190b2d78a5630bf6efc |
completed | April 9, 2026, 1:21 a.m. |
| PD | Predicate disambiguation | batch_69d6f30455888190b77f476b8418eaee |
completed | April 9, 2026, 12:29 a.m. |
Created at: April 8, 2026, 9:13 p.m.