Triple
T12143356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold Miner |
E289247
|
entity |
| Predicate | pointsPerGameCareerNBA |
P10820
|
FINISHED |
| Object | approximately 9.0 |
—
|
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: approximately 9.0 | Statement: [Harold Miner, pointsPerGameCareerNBA, approximately 9.0]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsPerGameCareerNBA Context triple: [Harold Miner, pointsPerGameCareerNBA, approximately 9.0]
-
A.
totalCareerPointsNBA
Indicates the total number of points a player has scored over their entire NBA career.
-
B.
NBA_careerPoints
Indicates the total number of points a player has scored over the course of their NBA career.
-
C.
careerPointsPerGame
chosen
Indicates the average number of points an individual scores per game over the course of their entire career.
-
D.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
E.
NBACareerGames
Indicates the total number of games an individual has played 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_69d6ab4c6710819097a9d228382dde43 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91841615c819097f20a7447a1b8f4 |
completed | April 10, 2026, 3:33 p.m. |
| PD | Predicate disambiguation | batch_69d91508f8008190b3a90ec0bf0953ca |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.