Triple
T13269817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Derrick Coleman |
E316024
|
entity |
| Predicate | pointsPerGameCareerAverageApprox |
P10820
|
FINISHED |
| Object | 16.5 |
—
|
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: 16.5 | Statement: [Derrick Coleman, pointsPerGameCareerAverageApprox, 16.5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsPerGameCareerAverageApprox Context triple: [Derrick Coleman, pointsPerGameCareerAverageApprox, 16.5]
-
A.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
B.
careerPointsPerGame
chosen
Indicates the average number of points an individual scores per game over the course of their entire career.
-
C.
careerBlocksPerGame
Indicates the average number of blocked shots a player records per game over the course of their entire career.
-
D.
totalCareerPointsNBA
Indicates the total number of points a player has scored over their entire NBA career.
-
E.
NBA_careerPoints
Indicates the total number of points a player has scored over the course of their NBA 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:26 p.m.