Triple
T37058254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Shot |
E917251
|
entity |
| Predicate | featuredPlayerPoints |
P18045
|
FINISHED |
| Object | 31 |
—
|
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: 31 | Statement: [The Shot, featuredPlayerPoints, 31]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuredPlayerPoints Context triple: [The Shot, featuredPlayerPoints, 31]
-
A.
featuresTopPlayers
Indicates that something prominently presents or showcases the most outstanding or highly ranked players.
-
B.
featuresTopPlayersFrom
Indicates that something includes or showcases the most prominent or best-performing players from a specified source or group.
-
C.
mostPointsPerGamePlayer
Indicates the player who has the highest average number of points scored per game within a given context or season.
-
D.
featuresPlayers
Indicates that something includes or showcases one or more players as part of its content or composition.
-
E.
topScorerPoints
chosen
Indicates the number of points scored by the top-scoring entity in a given context or event.
- 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_69f76e95fa40819091e14681087ae5e4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a037c8e2c648190a65fc9c7872861af |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a10036481909c71188b2a0e7f04 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:14 p.m.