Triple
T20271387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | EuroBasket 2015 |
E499100
|
entity |
| Predicate | PauGasolPointsPerGame |
P52811
|
FINISHED |
| Object | 25.6 |
—
|
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: 25.6 | Statement: [EuroBasket 2015, PauGasolPointsPerGame, 25.6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: PauGasolPointsPerGame Context triple: [EuroBasket 2015, PauGasolPointsPerGame, 25.6]
-
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.
PaulGeorgePoints
Indicates the number of points scored by Paul George in a game or over a specified period.
-
D.
scoringAverageOver30PPGSeason
Indicates that an entity (typically a player) has recorded a season with a scoring average exceeding 30 points per game.
-
E.
careerReboundsPerGame
Indicates the average number of rebounds a player records 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_69da6275fa6c8190952924930adee150 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e675de35188190840dc7d04c1d5fd9 |
completed | April 20, 2026, 6:52 p.m. |
| PD | Predicate disambiguation | batch_69e55b1e5e1c8190ba8a5544b1db9e1d |
completed | April 19, 2026, 10:45 p.m. |
Created at: April 11, 2026, 11:42 p.m.