Triple
T513380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red Auerbach |
E10653
|
entity |
| Predicate | yearsActiveAsCoach |
P15414
|
FINISHED |
| Object | 1946–1966 |
—
|
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: 1946–1966 | Statement: [Red Auerbach, yearsActiveAsCoach, 1946–1966]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: yearsActiveAsCoach Context triple: [Red Auerbach, yearsActiveAsCoach, 1946–1966]
-
A.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
B.
joinedTeamAsCoachYear
Indicates the year in which an individual joined a team specifically in the role of coach.
-
C.
activeYearsInSport
Indicates the span of years during which an entity actively participated in a particular sport.
-
D.
playerCoachChampionshipYears
Indicates the years in which a specific coach led a particular player to win a championship.
-
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. 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f232fa688190b08a2fe3f22c7a6e |
completed | Feb. 28, 2026, 1:48 p.m. |
| PD | Predicate disambiguation | batch_69a2f013c05481909e6dc87e7b20ebd8 |
completed | Feb. 28, 2026, 1:39 p.m. |
| PDg | Predicate description generation | batch_69a2f23214248190aa5fe1138f0f5a58 |
completed | Feb. 28, 2026, 1:48 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.