Triple
T18204278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RoBERTa |
E435864
|
entity |
| Predicate | pretrainingType |
P130207
|
FINISHED |
| Object | self-supervised learning |
—
|
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: self-supervised learning | Statement: [RoBERTa, pretrainingType, self-supervised learning]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pretrainingType Context triple: [RoBERTa, pretrainingType, self-supervised learning]
-
A.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
-
B.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
C.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
D.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
E.
canBeFineTuned
Indicates that one entity (typically a model or system) is capable of being further trained or adjusted using additional data or tasks to improve or specialize its behavior.
- F. None of above. chosen
Provenance (4 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. |
| PDg | Predicate description generation | batch_69e438f684e48190b38c64b58c518b6a |
completed | April 19, 2026, 2:07 a.m. |
Created at: April 10, 2026, 10:32 a.m.