Triple
T37158368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hill Street police station |
E920573
|
entity |
| Predicate | employsFictionalOfficer |
P31758
|
FINISHED |
| Object | Frank Furillo |
—
|
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: Frank Furillo | Statement: [Hill Street police station, employsFictionalOfficer, Frank Furillo]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employsFictionalOfficer Context triple: [Hill Street police station, employsFictionalOfficer, Frank Furillo]
-
A.
employsFictionalCharacter
Indicates that one entity (typically an organization or individual) has hired or uses the services of a fictional character in some capacity.
-
B.
hasFictionalSpy
Indicates that an entity includes, features, or is associated with a fictional spy character.
-
C.
isFictionalAgentOf
Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
-
D.
policeCharacter
chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
E.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
- 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_69f76ea0429081908c711b55599eac3c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fce28d6c3081908bf76f5db63ecf68 |
completed | May 7, 2026, 7:05 p.m. |
| PD | Predicate disambiguation | batch_69fce12d2f08819082134b5eb3db6a24 |
completed | May 7, 2026, 6:59 p.m. |
Created at: May 3, 2026, 4:15 p.m.