Triple
T22995152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mike Budenholzer |
E572167
|
entity |
| Predicate | NBAChampionshipsAsAssistantCoach |
P150013
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Mike Budenholzer, NBAChampionshipsAsAssistantCoach, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: NBAChampionshipsAsAssistantCoach Context triple: [Mike Budenholzer, NBAChampionshipsAsAssistantCoach, 4]
-
A.
numberOfNBATitlesAsAssistantCoach
chosen
Indicates the number of NBA championship titles an individual has won specifically in the role of an assistant coach.
-
B.
numberOfNBAChampionshipsAsHeadCoach
Indicates the count of NBA championship titles an individual has won while serving as a head coach.
-
C.
NBAChampionshipsAsExecutive
Indicates the number of NBA championships a person has won while serving in an executive or front-office role for a team.
-
D.
wonChampionshipAsAssistantCoachWith
Indicates that one entity served as an assistant coach on a team that won a championship together with the other entity.
-
E.
championshipWonAsCoach
Indicates that the subject, acting in the role of coach, has won a championship title with the associated team or organization.
- 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_69e245b535808190adef8a9df3c584db |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182f25af48190a98b7baeec824ae6 |
completed | April 29, 2026, 4:02 a.m. |
| PD | Predicate disambiguation | batch_69ef3b974e7c8190b8be11dbb4518693 |
completed | April 27, 2026, 10:33 a.m. |
Created at: April 17, 2026, 3:50 p.m.