Triple
T1873954
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mel Daniels |
E39095
|
entity |
| Predicate | numberOfABAChampionships |
P33401
|
FINISHED |
| Object | 3 |
—
|
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: 3 | Statement: [Mel Daniels, numberOfABAChampionships, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfABAChampionships Context triple: [Mel Daniels, numberOfABAChampionships, 3]
-
A.
wonABAChampionshipWith
Indicates that an entity secured an ABA championship title while being a member of, or associated with, a specified team or organization.
-
B.
SuperBowlChampionCount
Indicates the number of Super Bowl championships an entity (typically a team or franchise) has won.
-
C.
championshipsABAYears
Indicates the years in which entity A won championships associated with or against entity B.
-
D.
wonAFLChampionship
Indicates that the subject has won an AFL (Australian Football League) championship title.
-
E.
hasChampionships
Indicates that one entity possesses or has won one or more championships associated with another entity.
- 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_69a8862f7074819096afe7fe65e179e9 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb0f79fbc819085c54f3189a552d9 |
completed | March 7, 2026, 5 a.m. |
| PD | Predicate disambiguation | batch_69abafe2b56c81909e13d543982e6e13 |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb0f630e881908cd9491aeaeb4aed |
completed | March 7, 2026, 5 a.m. |
Created at: March 4, 2026, 7:34 p.m.