Triple
T38550508
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Earl Spilner |
E925096
|
entity |
| Predicate | employerUnderCover |
P129892
|
FINISHED |
| Object | Los Angeles 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: Los Angeles Police Department | Statement: [Brian Earl Spilner, employerUnderCover, Los Angeles Police Department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employerUnderCover Context triple: [Brian Earl Spilner, employerUnderCover, Los Angeles Police Department]
-
A.
employedIncognito
chosen
Indicates that one entity is working for or employed by another while deliberately concealing their true identity or role.
-
B.
employerInReality
Indicates that one entity is the actual, real-world employer of another entity, as opposed to a nominal, legal, or assumed employer.
-
C.
employerInPlot
Indicates that one entity serves as the employer of another within the context of a specific plot or storyline.
-
D.
employerIn
Indicates that one entity serves as the employer of another within a specified context, such as a location, organization, or time period.
-
E.
employedUnder
Indicates that one entity works as an employee under the authority, supervision, or organizational structure of another 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_69f76eaeb69c8190b367df9330d6f6af |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fd4f39b5008190b83b3227ce22c509 |
completed | May 8, 2026, 2:49 a.m. |
| PD | Predicate disambiguation | batch_69fd4df17c548190a4e2a6fea70f7e10 |
completed | May 8, 2026, 2:44 a.m. |
Created at: May 3, 2026, 4:32 p.m.