Triple
T29079001
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miss Israel 2004 |
E733920
|
entity |
| Predicate | winnerPrePageantOccupation |
P107055
|
FINISHED |
| Object | student |
—
|
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: student | Statement: [Miss Israel 2004, winnerPrePageantOccupation, student]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winnerPrePageantOccupation Context triple: [Miss Israel 2004, winnerPrePageantOccupation, student]
-
A.
winnerProfession
chosen
Indicates that the associated profession is the occupation or field of work of the winner in a given event or competition.
-
B.
notableInternationalPageant
Indicates that the subject is a notable or significant international beauty pageant.
-
C.
mostFamousBearerOccupation
Indicates that the occupation specified is the primary or best-known profession of the most famous individual associated with a given name or entity.
-
D.
laureateOccupation
Indicates the professional role or field in which a laureate is recognized or has worked.
-
E.
nominatedPersonOccupation
Indicates that the nominated person holds or is associated with a particular occupation or professional role.
- 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_69f05b0c0f28819086eae6e84f2ae472 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f707f7959881908f037f0d6b1d0c36 |
completed | May 3, 2026, 8:31 a.m. |
| PD | Predicate disambiguation | batch_69f700fc274c8190a128593dc7c7abd0 |
completed | May 3, 2026, 8:02 a.m. |
Created at: April 28, 2026, 10:52 a.m.