Triple
T18205066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeiT |
E435881
|
entity |
| Predicate | teacherModelType |
P121953
|
FINISHED |
| Object | convolutional neural network |
—
|
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: convolutional neural network | Statement: [DeiT, teacherModelType, convolutional neural network]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teacherModelType Context triple: [DeiT, teacherModelType, convolutional neural network]
-
A.
hasTeacherType
chosen
Indicates that an entity is associated with a teacher characterized by a specific type or category (e.g., role, specialization, or employment status).
-
B.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
C.
typeOfTeaching
Indicates the specific method or style of teaching used in an instructional context.
-
D.
teacherOrInfluence
Indicates that one entity serves as a teacher to, or has a significant influence on the development, behavior, or thinking of, another entity.
-
E.
hasTeacher
Indicates that one entity serves as an instructor or educator for 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.