Triple
T36420121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vonnie |
E897127
|
entity |
| Predicate | associatedWithSportOfBearer |
P204867
|
FINISHED |
| Object | American football |
—
|
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: American football | Statement: [Vonnie, associatedWithSportOfBearer, American football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithSportOfBearer Context triple: [Vonnie, associatedWithSportOfBearer, American football]
-
A.
associatedWithTeamSport
Indicates a relationship where an entity is connected to, involved in, or participates in a team-based sport.
-
B.
hasAssociatedSportsIdentity
Indicates that one entity is linked to another entity that represents its identity or role within a sports context.
-
C.
associatedWithSportMarket
Indicates a relationship where an entity is connected or linked to a specific sport-related market or segment within the sports industry.
-
D.
hasAssociatedSportFigure
Indicates that an entity is linked or related to a particular sports figure (such as an athlete, coach, or sports personality).
-
E.
associatedWithStyleOfPlayOfBearer
Indicates that something is connected to or characterized by the style of play exhibited by the bearer.
- 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_69f76e559b10819099d6655a6e14587c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a037c92f03c8190ae2751270b195423 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0a54cc8190868c1bfa1590d1a6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c82f8c88190bd77a086023ac0e1 |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:10 p.m.