Triple
T20643707
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1950–51 NBA season |
E507295
|
entity |
| Predicate | averagePointsPerGameLeader |
P52811
|
FINISHED |
| Object | 28.4 |
—
|
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: 28.4 | Statement: [1950–51 NBA season, averagePointsPerGameLeader, 28.4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: averagePointsPerGameLeader Context triple: [1950–51 NBA season, averagePointsPerGameLeader, 28.4]
-
A.
pointsPerGame
chosen
Indicates the average number of points an entity scores per game over a given set of games.
-
B.
CBASeasonPointsPerGameLeader
Indicates the player who led the CBA in average points scored per game for a given season.
-
C.
collegeTeamPointsPerGameLeader
Indicates the player who leads a college team in average points scored per game.
-
D.
NBASeasonScoringLeader
Indicates that the subject was the player who scored the most total points in a given NBA season.
-
E.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
- 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_69e0b4be702c8190a3d2410a881d310a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6af1d0be481909e090193dcfd9cf6 |
completed | April 20, 2026, 10:56 p.m. |
| PD | Predicate disambiguation | batch_69e5c0315f5081908098707c6455e56e |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 11:43 a.m.