Triple
T75465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinti |
E1508
|
entity |
| Predicate | traditionalOccupation |
P2374
|
FINISHED |
| Object | musician |
—
|
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: musician | Statement: [Sinti, traditionalOccupation, musician]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionalOccupation Context triple: [Sinti, traditionalOccupation, musician]
-
A.
subjectOccupation
chosen
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
-
B.
victimOccupation
Indicates the profession or job role held by the person who is the victim in an event or incident.
-
C.
typeOfWork
Indicates the kind or category of work associated with or performed by an entity.
-
D.
employedPeople
Indicates that there exists a relationship where people are currently working in jobs or positions, typically under an employer.
-
E.
employerType
Indicates the classification or category of an employer in relation to the entity (e.g., public, private, nonprofit, self-employed).
- 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_69a24c60d19c8190a1b6c105ca59ef5b |
completed | Feb. 28, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69a25314bd6c81908d1cfd4b83f20049 |
completed | Feb. 28, 2026, 2:29 a.m. |
| PD | Predicate disambiguation | batch_69a24eae77ec81909015906f31f2b62e |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:06 a.m.