Triple
T22333431
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FIBA Basketball World Cup MVP 2019 |
E552080
|
entity |
| Predicate | winnerPointsPerGameApprox |
P52811
|
FINISHED |
| Object | 16.4 |
—
|
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: 16.4 | Statement: [FIBA Basketball World Cup MVP 2019, winnerPointsPerGameApprox, 16.4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: winnerPointsPerGameApprox Context triple: [FIBA Basketball World Cup MVP 2019, winnerPointsPerGameApprox, 16.4]
-
A.
pointsPerGame
chosen
Indicates the average number of points an entity scores per game over a given set of games.
-
B.
winnerPoints
Indicates the number of points earned by the winning participant or entity in a competition or event.
-
C.
careerPointsPerGame
Indicates the average number of points an individual scores per game over the course of their entire career.
-
D.
pointsScored
Indicates the number of points an entity has earned or achieved in a particular event, game, or context.
-
E.
pointsScoredInNBA
Indicates the number of points a player has scored in NBA games.
- 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577cdcb08190a760e195c1051adb |
completed | April 29, 2026, 12:57 a.m. |
| PD | Predicate disambiguation | batch_69e73004d9e88190bb862319a5aea06b |
completed | April 21, 2026, 8:06 a.m. |
Created at: April 16, 2026, 8:43 p.m.