Triple
T33103525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shelbyville |
E847122
|
entity |
| Predicate | hasPoliceForceInFiction |
P95623
|
FINISHED |
| Object | Shelbyville Police Department |
—
|
NE NERFINISHED |
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: Shelbyville Police Department | Statement: [Shelbyville, hasPoliceForceInFiction, Shelbyville Police Department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPoliceForceInFiction Context triple: [Shelbyville, hasPoliceForceInFiction, Shelbyville Police Department]
-
A.
hasFictionalPoliceDepartment
chosen
Indicates that an entity is associated with or features a police department that exists only within a fictional or imaginary context.
-
B.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
C.
policeCharacter
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
D.
hasPoliceInstitution
Indicates that an entity is associated with, governed by, or served by a particular police institution or law enforcement body.
-
E.
hasOwnPoliceForce
Indicates that an entity maintains and controls its own dedicated police force or law enforcement agency.
- 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_69f3495686508190b76bf20fa5e00bf7 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69fed83b1d188190a318b0ad3003200a |
completed | May 9, 2026, 6:46 a.m. |
| PD | Predicate disambiguation | batch_69fed78e03548190b6e6ad93ae8d131d |
completed | May 9, 2026, 6:43 a.m. |
Created at: May 1, 2026, 1:26 a.m.