Triple
T17441916
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Nicklaus course |
E424676
|
entity |
| Predicate | hasDesignerOccupation |
P12117
|
FINISHED |
| Object | professional golfer |
—
|
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: professional golfer | Statement: [Jack Nicklaus course, hasDesignerOccupation, professional golfer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDesignerOccupation Context triple: [Jack Nicklaus course, hasDesignerOccupation, professional golfer]
-
A.
designerOccupation
chosen
Indicates that one entity serves as the professional designer or design specialist for another entity.
-
B.
hasOccupationOfDesignee
Indicates that one entity serves as the designated or appointed holder of an occupation or role for another entity.
-
C.
designerEmployer
Indicates that one entity is the employer or employing organization of a designer in relation to a design activity or role.
-
D.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
E.
creatorOccupation
Indicates the professional role or job that the creator of an entity holds or held.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44ff82bbc819095dd0621137da809 |
completed | April 19, 2026, 3:46 a.m. |
| PD | Predicate disambiguation | batch_69e3b030eac481909b8402719cc3102e |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:46 a.m.