Triple
T37538806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Exodar |
E933274
|
entity |
| Predicate | hasProfessionTrainers |
—
|
GENERATED |
| Object | yes |
—
|
UNRECOGNIZED GENERATED |
How this triple was built (1 step)
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.
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionTrainers Context triple: [Exodar, hasProfessionTrainers, yes]
-
A.
hasClassTrainersFor
Indicates that an entity provides or is associated with trainers responsible for conducting or leading a particular class.
-
B.
hasClassTrainers
Indicates that a class or course is associated with one or more trainers responsible for conducting or leading it.
-
C.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
D.
trainedAs
Indicates that one entity has received education or instruction to perform the role, profession, or function represented by another entity.
-
E.
providesTrainingFor
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
- F. None of above. chosen
Provenance (1 batch)
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_69f76ec999288190ae26ec7b6aea7046 |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:17 p.m.