Triple
T6482808
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carousel Club |
E146433
|
entity |
| Predicate | hasOwnerProfession |
P35215
|
FINISHED |
| Object | nightclub operator |
—
|
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: nightclub operator | Statement: [Carousel Club, hasOwnerProfession, nightclub operator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOwnerProfession Context triple: [Carousel Club, hasOwnerProfession, nightclub operator]
-
A.
isAssociatedWithProfessionOfBearer
chosen
Indicates that one entity is connected to, or involved with, the profession or occupational role held by another entity.
-
B.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
-
C.
includesProfession
Indicates that one entity’s set of attributes, roles, or members contains a specific profession as part of it.
-
D.
memberProfession
Indicates that a member or individual holds or practices a particular profession or occupation.
-
E.
hasRegulatedProfession
Indicates that an entity practices or is associated with a profession that is formally regulated by laws, standards, or licensing authorities.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a6cd0c4819085a921e6a361d91c |
completed | March 22, 2026, 10:17 p.m. |
| PD | Predicate disambiguation | batch_69c0673f6d48819080e10c85155c7195 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:51 p.m.