Triple
T9805847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Twyman |
E237950
|
entity |
| Predicate | pointsScoredInNBA |
P90073
|
FINISHED |
| Object | 15740 |
—
|
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: 15740 | Statement: [Jack Twyman, pointsScoredInNBA, 15740]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsScoredInNBA Context triple: [Jack Twyman, pointsScoredInNBA, 15740]
-
A.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
B.
careerPointsABA
Indicates the total number of points a player has scored over their career in the ABA (American Basketball Association).
-
C.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
D.
NBASeasonScoringLeader
Indicates that the subject was the player who scored the most total points in a given NBA season.
-
E.
maximumScorePerDunk
Indicates the highest number of points that can be awarded for a single dunk action in the given context.
- 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_69ca84dd4608819097ff4ed00feca280 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdab7b67748190ba16ce868f29d13e |
completed | April 1, 2026, 11:34 p.m. |
| PD | Predicate disambiguation | batch_69cd03dd2da881909052fbf29736a773 |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06abc9248190a506b64e9c516d03 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:29 p.m.