Triple
T24036781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College of Dental Medicine–Arizona |
E595253
|
entity |
| Predicate | trainsProfessionalsFor |
P40765
|
FINISHED |
| Object | dentist |
—
|
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: dentist | Statement: [College of Dental Medicine–Arizona, trainsProfessionalsFor, dentist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainsProfessionalsFor Context triple: [College of Dental Medicine–Arizona, trainsProfessionalsFor, dentist]
-
A.
providesTrainingFor
chosen
Indicates that one entity delivers or conducts training activities intended to develop the skills or knowledge of another entity.
-
B.
alsoTrains
Indicates that an entity, in addition to its primary role or activity, is involved in training another entity.
-
C.
trainingIn
Indicates that one entity is undergoing or receiving training within the context, program, or domain specified by another entity.
-
D.
maintainsTrainsFor
Indicates that one entity is responsible for servicing, repairing, or otherwise keeping trains operational for another entity.
-
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_69e288bf45f08190a1b6ed8cd0b9e86b |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d8d5fdc48190a037cf9447309356 |
completed | April 29, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:56 p.m.