Triple
T21389574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray Meyer |
E527604
|
entity |
| Predicate | coachingCareerSpan |
P15414
|
FINISHED |
| Object | 1942–1984 |
—
|
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: 1942–1984 | Statement: [Ray Meyer, coachingCareerSpan, 1942–1984]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coachingCareerSpan Context triple: [Ray Meyer, coachingCareerSpan, 1942–1984]
-
A.
coachingEra
Indicates the time period during which a particular coach is in charge of a team or individual.
-
B.
yearsActiveAsCoach
chosen
Indicates the span of time, typically in years, during which an individual has served in a coaching role.
-
C.
hasCoachedFor
Indicates that one entity has served in a coaching role for another entity, such as a team, organization, or individual.
-
D.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
E.
hasCareerSpanCoverage
Indicates that one entity’s coverage, record, or data extends across the full duration of another entity’s career span.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f8ae288190b43df9fe2841a822 |
completed | April 22, 2026, 11:28 a.m. |
| PD | Predicate disambiguation | batch_69e6162bbfc88190a3e75859941b2638 |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 5:13 p.m.