Triple
T30627859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIBA Basketball World Cup MVP 2023 |
E779630
|
entity |
| Predicate | winnerJerseyNumberNationalTeam |
P156847
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [FIBA Basketball World Cup MVP 2023, winnerJerseyNumberNationalTeam, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winnerJerseyNumberNationalTeam Context triple: [FIBA Basketball World Cup MVP 2023, winnerJerseyNumberNationalTeam, 17]
-
A.
playsForNationalTeamShirtNumber
chosen
Indicates the shirt number a player wears when representing their national team in official matches.
-
B.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
jerseyNumberTeam
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
-
D.
previousNationalTeamEntity
Indicates that an entity previously represented a particular national team before its current or later affiliation.
-
E.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
- 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_69f224a431548190a44ad9d088dbf91f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f73223675481908c1bc3208c0f5284 |
completed | May 3, 2026, 11:31 a.m. |
| PD | Predicate disambiguation | batch_69f7317690108190b3aae2cd2e1d069e |
completed | May 3, 2026, 11:28 a.m. |
Created at: April 29, 2026, 8:28 p.m.