Triple
T25588687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fenerbahçe Women’s Volleyball |
E641459
|
entity |
| Predicate | associatedBasketballClub |
P193342
|
FINISHED |
| Object | Fenerbahçe men’s basketball |
—
|
NE NERFINISHED |
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: Fenerbahçe men’s basketball | Statement: [Fenerbahçe Women’s Volleyball, associatedBasketballClub, Fenerbahçe men’s basketball]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedBasketballClub Context triple: [Fenerbahçe Women’s Volleyball, associatedBasketballClub, Fenerbahçe men’s basketball]
-
A.
preNBAClub
Indicates that a person was associated with a particular basketball club or team before joining the NBA.
-
B.
basketballTeamOf
Indicates that one entity is the basketball team to which another entity belongs or is affiliated.
-
C.
associatedClub1
Indicates that an entity has a primary or first-listed association with a particular club or organization.
-
D.
associatedClub2
Indicates that an entity has a secondary or additional affiliation or membership with a particular club.
-
E.
associatedClubSport
Indicates that there is a relationship between a club and the sport with which it is connected or aligned.
- F. None of above. chosen
Provenance (4 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_69e75dc42b588190a98b58e0df359674 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69fd44474ed48190ac372e4c88d762ed |
completed | May 8, 2026, 2:02 a.m. |
| PD | Predicate disambiguation | batch_69fd41ef28a48190a66959be5c964461 |
completed | May 8, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69fd4445f8c08190bb2dc27e0971c55d |
completed | May 8, 2026, 2:02 a.m. |
Created at: April 21, 2026, 4:18 p.m.