Triple
T5833075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Kaline |
E129397
|
entity |
| Predicate | playedCareerStartYear |
P67438
|
FINISHED |
| Object | 1953 |
—
|
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: 1953 | Statement: [Al Kaline, playedCareerStartYear, 1953]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: playedCareerStartYear Context triple: [Al Kaline, playedCareerStartYear, 1953]
-
A.
activeYearsInCareer
Indicates the span of time during which an entity was actively engaged in a particular career or professional field.
-
B.
retirementYearAsPlayer
Indicates the year in which an individual ended their career as an active player.
-
C.
playedEntireCareerForSingleFranchise
Indicates that an athlete spent their entire professional career playing for only one franchise or team.
-
D.
careerSeasons
Indicates the number or set of seasons during which an entity actively participated in a particular career or professional role.
-
E.
careerGamesStarted
Indicates the total number of games an entity has started over the course of its entire career.
- 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_69c0084af79c81908af128ccc29983d0 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03341e5888190a5f219b6f92cb161 |
completed | March 22, 2026, 6:21 p.m. |
| PDg | Predicate description generation | batch_69c044a9c4f0819081b8c196932883f6 |
completed | March 22, 2026, 7:36 p.m. |
Created at: March 22, 2026, 3:54 p.m.