Triple
T32752646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aviron Bayonnais |
E837538
|
entity |
| Predicate | hasProfessionalSquad |
P203024
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Aviron Bayonnais, hasProfessionalSquad, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalSquad Context triple: [Aviron Bayonnais, hasProfessionalSquad, yes]
-
A.
hasProfessionalPlayers
Indicates that an entity is associated with or includes individuals who participate in a profession at a professional level.
-
B.
hostsProfessionalTeam
Indicates that one entity serves as the home base or venue for a professional sports team.
-
C.
professionalTeam
Indicates that one entity is a professional sports team associated with, representing, or employing the other entity.
-
D.
ownsProfessionalTeam
Indicates that one entity has legal ownership or controlling interest in a professional sports team.
-
E.
hasSubteam
Indicates that one team is a subordinate or component team within another, larger team.
- 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_69f34937f97c8190b7f84bea045df3ae |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_6a0115fb84448190b8b67a5ace7b289a |
completed | May 10, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_6a0114ef60a081908f8db7868cf29b2f |
completed | May 10, 2026, 11:29 p.m. |
| PDg | Predicate description generation | batch_6a0115fadcec8190bd1be2b44c1fc397 |
completed | May 10, 2026, 11:34 p.m. |
Created at: May 1, 2026, 1:12 a.m.