Triple
T6438406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Major League Baseball umpires |
E129956
|
entity |
| Predicate | typicallyTrainedAt |
P2858
|
FINISHED |
| Object | professional umpire school |
—
|
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: professional umpire school | Statement: [Major League Baseball umpires, typicallyTrainedAt, professional umpire school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicallyTrainedAt Context triple: [Major League Baseball umpires, typicallyTrainedAt, professional umpire school]
-
A.
trainingInstitution
chosen
Indicates that one entity serves as the institution or organization where another entity receives training or education.
-
B.
countryOfTraining
Indicates the country in which an entity received its training or education.
-
C.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
D.
leadsToTrainingAt
Indicates that one entity causes, results in, or serves as a pathway to another entity undergoing training.
-
E.
studiedUnder
Indicates that one entity received instruction, training, or mentorship from another, typically in an academic or apprenticeship 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06965a5d48190a5860da9e22dc6e0 |
completed | March 22, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69c060f96980819091bab9335922a457 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:45 p.m.