Triple
T30512550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Skating Club of Boston |
E776467
|
entity |
| Predicate | trainsAthleteType |
P33092
|
FINISHED |
| Object | single skaters |
—
|
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: single skaters | Statement: [The Skating Club of Boston, trainsAthleteType, single skaters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsAthleteType Context triple: [The Skating Club of Boston, trainsAthleteType, single skaters]
-
A.
typeOfAthlete
chosen
Indicates that one entity is an athlete and the other specifies the kind or category of athlete they are.
-
B.
trainsetType
Indicates the specific category or role of a dataset within a training process (e.g., training, validation, or test set).
-
C.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
D.
usesCoachType
Indicates that an entity employs or operates a specific type or category of coach (e.g., vehicle or carriage) in its service or context.
-
E.
trainsInDiscipline
Indicates that one entity undergoes training or instruction within a particular discipline, field, or area of expertise associated with another entity.
- 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_69f2249a155c8190b1d512106007e9bb |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6b903538481909cffcb6cc1cc0e70 |
completed | May 3, 2026, 2:54 a.m. |
| PD | Predicate disambiguation | batch_69f6b626120c819097c9ad04487570d7 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 29, 2026, 8:16 p.m.