Triple
T26718925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stana Katic as Kate Beckett |
E673645
|
entity |
| Predicate | policeDepartmentInSeries |
P31758
|
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: [Stana Katic as Kate Beckett, policeDepartmentInSeries, NYPD 12th Precinct]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policeDepartmentInSeries Context triple: [Stana Katic as Kate Beckett, policeDepartmentInSeries, NYPD 12th Precinct]
-
A.
policeDepartmentType
Indicates the specific organizational category or classification of a police department (e.g., municipal, state, federal).
-
B.
hasPoliceDepartment
Indicates that an entity possesses, is served by, or is administratively associated with a police department.
-
C.
hasFictionalPoliceDepartment
Indicates that an entity is associated with or features a police department that exists only within a fictional or imaginary context.
-
D.
policeCharacter
chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
E.
policeBureau
Indicates that an entity functions as or is associated with a police bureau or police department.
- 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_69eecda481d08190aea69f2f7c745f56 |
completed | April 27, 2026, 2:44 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 27, 2026, 3:39 a.m.