Triple
T17793564
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 99th Precinct |
E444229
|
entity |
| Predicate | fictionalDepartmentType |
P78750
|
FINISHED |
| Object | detective squad |
—
|
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 squad | Statement: [99th Precinct, fictionalDepartmentType, detective squad]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalDepartmentType Context triple: [99th Precinct, fictionalDepartmentType, detective squad]
-
A.
departmentType
Indicates the classification or category of a department, specifying what kind of department it is.
-
B.
hasDepartmentInFiction
chosen
Indicates that a fictional work includes or features a specific department as part of its setting or narrative.
-
C.
fictionalEntityType
Indicates that the subject is classified as a particular type or category of fictional entity within a narrative or imaginary context.
-
D.
fictionalField
Indicates that the subject is associated with a fictional or imaginary field, domain, or area rather than a real-world one.
-
E.
department
Indicates that one entity functions as an organizational unit or division within another, typically larger, entity.
- 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_69d8b9efe370819095cd219b143ae727 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487993a6c8190805e06d93dfc0dce |
completed | April 19, 2026, 7:43 a.m. |
| PD | Predicate disambiguation | batch_69e3d8d8e538819084f1584426b41d5e |
completed | April 18, 2026, 7:17 p.m. |
Created at: April 10, 2026, 10:13 a.m.