Triple
T30688563
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jamie Reagan |
E781253
|
entity |
| Predicate | worksInFictionalDepartment |
P109189
|
FINISHED |
| Object | NYPD 12th Precinct |
—
|
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: NYPD 12th Precinct | Statement: [Jamie Reagan, worksInFictionalDepartment, NYPD 12th Precinct]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: worksInFictionalDepartment Context triple: [Jamie Reagan, worksInFictionalDepartment, NYPD 12th Precinct]
-
A.
hasDepartmentInFiction
Indicates that a fictional work includes or features a specific department as part of its setting or narrative.
-
B.
worksForFictionalOrganization
chosen
Indicates that an entity is employed by or affiliated as a worker with a fictional organization.
-
C.
worksInDepartment
Indicates that an entity is employed in and performs their job duties within a particular department.
-
D.
worksAtFictionalPlace
Indicates that an entity is employed at or associated with performing work in a fictional or imaginary location.
-
E.
worksInFictionalContext
Indicates that an entity performs work or fulfills a role within a fictional or imagined setting rather than in real-world circumstances.
- 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_69f224a92f54819095499b4d32bd5134 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f7308a096081909d66a56f3c926806 |
completed | May 3, 2026, 11:24 a.m. |
| PD | Predicate disambiguation | batch_69f72a00c5f081908b6539d15baf4e12 |
completed | May 3, 2026, 10:57 a.m. |
Created at: April 29, 2026, 8:33 p.m.