Triple
T31572737
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiki Cuyler |
E805608
|
entity |
| Predicate | sportNumberOfGamesPlayed |
P3204
|
FINISHED |
| Object | over 1900 MLB games |
—
|
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: over 1900 MLB games | Statement: [Kiki Cuyler, sportNumberOfGamesPlayed, over 1900 MLB games]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportNumberOfGamesPlayed Context triple: [Kiki Cuyler, sportNumberOfGamesPlayed, over 1900 MLB games]
-
A.
sportNumberOfAppearances
Indicates the total number of times an entity has participated in or appeared in a particular sport or sporting event.
-
B.
sportsPlayed
Indicates that an entity participates in or engages in a particular sport.
-
C.
gamesOf
Indicates a relationship where one entity is the set, list, or collection of games associated with another entity.
-
D.
gamesPlayed
chosen
Indicates the number or set of games that an entity has participated in or completed.
-
E.
sportNumber
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
- 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_69f348d3a86c8190a3e5e539a4dd125f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6bbbef7a88190b0affdec1d41c1e0 |
completed | May 3, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6cef208190bc5cd43d96127004 |
completed | May 3, 2026, 3:01 a.m. |
Created at: April 30, 2026, 10:20 p.m.