Triple
T7611652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Artis Gilmore |
E172252
|
entity |
| Predicate | ABAFieldGoalPercentageLeader |
P77767
|
FINISHED |
| Object | multiple seasons |
—
|
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: multiple seasons | Statement: [Artis Gilmore, ABAFieldGoalPercentageLeader, multiple seasons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ABAFieldGoalPercentageLeader Context triple: [Artis Gilmore, ABAFieldGoalPercentageLeader, multiple seasons]
-
A.
ledLeagueInReceivingTouchdowns
Indicates that the subject had the highest number of receiving touchdowns in the league for a given season or time period.
-
B.
gameWinningFieldGoalDistance
Indicates the distance from which a decisive, game-winning field goal was successfully kicked.
-
C.
gameWinningTouchdownPasser
Indicates that the subject is the player who threw the touchdown pass that secured the victory in the game for the subject's team.
-
D.
criterionFieldGoalPercentageUnit
Indicates the unit of measurement used to express a field goal percentage criterion in a given context.
-
E.
nflAllTimePointsLeader
Indicates that the subject is the player who holds the record for the most career points scored in NFL history.
- 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_69c6994f50808190ba228764bb422417 |
completed | March 27, 2026, 2:50 p.m. |
| NER | Named-entity recognition | batch_69c6fa221c848190b892ba1caec8d83a |
completed | March 27, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e485f88190910b39da52a955fe |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f8195e5c8190835e28d44e19f6ef |
completed | March 27, 2026, 9:35 p.m. |
Created at: March 27, 2026, 3:55 p.m.