Triple
T32980237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Commonwealth Games mixed doubles |
E843775
|
entity |
| Predicate | maxPointsPerGame |
P175457
|
FINISHED |
| Object | 30 points |
—
|
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: 30 points | Statement: [Commonwealth Games mixed doubles, maxPointsPerGame, 30 points]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maxPointsPerGame Context triple: [Commonwealth Games mixed doubles, maxPointsPerGame, 30 points]
-
A.
mostPointsPerGamePlayer
Indicates the player who has the highest average number of points scored per game within a given context or season.
-
B.
pointsPerGame
Indicates the average number of points an entity scores per game over a given set of games.
-
C.
maximumScorePerDunk
Indicates the highest number of points that can be awarded for a single dunk action in the given context.
-
D.
scored70PointsInSingleGame
Indicates that an entity achieved a total of 70 points in a single game or match.
-
E.
scored71PointsInSingleGame
Indicates that an entity achieved a total of 71 points in a single game.
- F. None of above. chosen
Provenance (4 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_69f3494c6f9c8190a255409fce8b1d3b |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6d1dbc8508190afb60638af280c54 |
completed | May 3, 2026, 4:40 a.m. |
| PD | Predicate disambiguation | batch_69f6cfe5f93c8190995c53dbbe380a32 |
completed | May 3, 2026, 4:32 a.m. |
| PDg | Predicate description generation | batch_69f6d0d331dc8190be5aa6bfc6365e67 |
completed | May 3, 2026, 4:36 a.m. |
Created at: May 1, 2026, 1:22 a.m.