Triple
T26064304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Robbie Ray |
E657356
|
entity |
| Predicate | hasJerseyNumberWithTeam |
P104927
|
FINISHED |
| Object | 38 for Toronto Blue Jays |
—
|
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: 38 for Toronto Blue Jays | Statement: [Robbie Ray, hasJerseyNumberWithTeam, 38 for Toronto Blue Jays]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasJerseyNumberWithTeam Context triple: [Robbie Ray, hasJerseyNumberWithTeam, 38 for Toronto Blue Jays]
-
A.
jerseyNumberTeam
chosen
Indicates the association between a specific jersey number and the team for which that jersey number is used or assigned.
-
B.
hasJerseyColor
Indicates that an entity’s jersey possesses or is characterized by a specific color.
-
C.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
D.
hasJersey
Indicates that one entity possesses or is associated with a particular jersey.
-
E.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
- 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_69ee5bbd788481909e22bd7153d0c037 |
completed | April 26, 2026, 6:38 p.m. |
| NER | Named-entity recognition | batch_69f606954f288190bcb77d432ed3617c |
completed | May 2, 2026, 2:13 p.m. |
| PD | Predicate disambiguation | batch_69f5aff889988190ad10bcf1a280f717 |
completed | May 2, 2026, 8:04 a.m. |
Created at: April 26, 2026, 7:21 p.m.