Triple
T37859053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monta Ellis |
E944271
|
entity |
| Predicate | wearsNumberForTeam (Dallas Mavericks) |
P104927
|
FINISHED |
| Object | 11 |
—
|
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: 11 | Statement: [Monta Ellis, wearsNumberForTeam (Dallas Mavericks), 11]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wearsNumberForTeam (Dallas Mavericks) Context triple: [Monta Ellis, wearsNumberForTeam (Dallas Mavericks), 11]
-
A.
wearsNumberForPosition
Indicates that an entity wears a specific jersey or identification number associated with a particular position or role.
-
B.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
-
D.
wearsNumberRetiredBy
Indicates that one entity wears a jersey number that has been officially retired in honor of another entity.
-
E.
jerseyNumberTeam
chosen
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
- 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_69f76eee2f9c8190b1272aa2ee55ebf5 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fbc36ce1f88190a7fa1656b714e107 |
completed | May 6, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69fbbd166a488190b1bf9316b0790801 |
completed | May 6, 2026, 10:13 p.m. |
Created at: May 3, 2026, 4:19 p.m.