Triple
T15389691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jerry Stackhouse |
E368006
|
entity |
| Predicate | pointsPerGameIn1999-2000Season |
P52811
|
FINISHED |
| Object | 23.6 |
—
|
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: 23.6 | Statement: [Jerry Stackhouse, pointsPerGameIn1999-2000Season, 23.6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsPerGameIn1999-2000Season Context triple: [Jerry Stackhouse, pointsPerGameIn1999-2000Season, 23.6]
-
A.
pointsPerGame
chosen
Indicates the average number of points an entity scores per game over a given set of games.
-
B.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
C.
pointsScoredInNBA
Indicates the number of points a player has scored in NBA games.
-
D.
scoredPointsPerGameInCollege
Indicates the average number of points an entity (typically an athlete) scored per game during their college career.
-
E.
playedBasketballFor
Indicates that one entity was a member of and competed for another entity’s basketball team.
- 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_69d85a1551a08190ba2caea7cd51c639 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e7727a081908eff45bbc1633c8a |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27742a881909cd73cc5c7d062fd |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:19 a.m.