Triple
T22909731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | U.S. Women’s Open |
E568556
|
entity |
| Predicate | courseRotation |
P13372
|
FINISHED |
| Object | rotating venues |
—
|
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: rotating venues | Statement: [U.S. Women’s Open, courseRotation, rotating venues]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: courseRotation Context triple: [U.S. Women’s Open, courseRotation, rotating venues]
-
A.
courseRotationName
Indicates the specific name or label assigned to a particular rotation or offering of a course within a scheduling or curriculum cycle.
-
B.
courseRenovation
Indicates that a course is undergoing or has undergone changes or improvements to its structure, content, or delivery.
-
C.
courseSetting
Indicates the context or environment in which a course is delivered or conducted.
-
D.
courseVariation
Indicates that one course is an alternative or variant version of another course, differing in some aspects such as content, format, or level while remaining related.
-
E.
rotatesAmong
chosen
Indicates that an entity takes turns occupying or performing a role, position, or function in sequence with other entities.
- 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_69e2458cd9e48190943ad2e34485d939 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1807350008190a057e8fb5c363c5f |
completed | April 29, 2026, 3:52 a.m. |
| PD | Predicate disambiguation | batch_69ef3b6b2e2481908258156937b5a745 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:42 p.m.