Triple
T9229166
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Brisker |
E221769
|
entity |
| Predicate | pointsPerGameABA |
P52811
|
FINISHED |
| Object | over 20 points per game in 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: over 20 points per game in multiple seasons | Statement: [John Brisker, pointsPerGameABA, over 20 points per game in multiple seasons]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsPerGameABA Context triple: [John Brisker, pointsPerGameABA, over 20 points per game in multiple seasons]
-
A.
pointsPerGame
chosen
Indicates the average number of points an entity scores per game over a given set of games.
-
B.
careerPointsABA
Indicates the total number of points a player has scored over their career in the ABA (American Basketball Association).
-
C.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
D.
leagueScoringRankABAAllTime
Indicates a subject’s all-time scoring rank within the American Basketball Association (ABA) across the entire history of the league.
-
E.
ABACareerReboundsLeader
Indicates that the subject holds the record for the most total rebounds accumulated over their entire career in ABA competition.
- 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_69ca83ec8db08190a9110df8232885d2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccdaa1c5b4819081dac6713053a8ae |
completed | April 1, 2026, 8:43 a.m. |
| PD | Predicate disambiguation | batch_69cc7a3daeb481908b0abde3fbc1f1f0 |
completed | April 1, 2026, 1:51 a.m. |
Created at: March 30, 2026, 7:29 p.m.