Triple
T33331197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Bibby |
E853409
|
entity |
| Predicate | jerseyNumberAssociatedWithCareer |
P2651
|
FINISHED |
| Object | 10 |
—
|
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: 10 | Statement: [Mike Bibby, jerseyNumberAssociatedWithCareer, 10]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: jerseyNumberAssociatedWithCareer Context triple: [Mike Bibby, jerseyNumberAssociatedWithCareer, 10]
-
A.
hasJerseyNumberRetired
Indicates that an entity has had its jersey number officially retired, typically in recognition of its contributions or achievements.
-
B.
jerseyNumberRetiredInHisHonor
Indicates that a person’s jersey number has been officially retired by a team or organization as a tribute to that person’s contributions or legacy.
-
C.
jerseyNumber
chosen
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
D.
jerseyNumberTeam
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
-
E.
retiredJerseyNumberByTeam
Indicates that a sports team has officially retired a specific jersey number in honor of a player or figure, making it unavailable for future use by team members.
- 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_69f34969614c81909cd99661b0902533 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e3156ea48190b604e414665ef351 |
completed | May 3, 2026, 5:54 a.m. |
| PD | Predicate disambiguation | batch_69f6de0b9ba48190887c9eb5d06a2e94 |
completed | May 3, 2026, 5:32 a.m. |
Created at: May 1, 2026, 1:34 a.m.