Triple
T18204242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RoBERTa |
E435864
|
entity |
| Predicate | trainingDataScale |
P48407
|
FINISHED |
| Object | larger than BERT |
—
|
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: larger than BERT | Statement: [RoBERTa, trainingDataScale, larger than BERT]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingDataScale Context triple: [RoBERTa, trainingDataScale, larger than BERT]
-
A.
trainingDataType
Indicates the type or category of data used for training a model, system, or process.
-
B.
trainingDatasetSize
chosen
Indicates the number of data samples or instances used to train a model or system.
-
C.
requiresFeatureScaling
Indicates that applying feature scaling is a necessary preprocessing step for the associated data or model.
-
D.
trainingDataSource
Indicates the origin or provider from which the training data for a model or system is obtained.
-
E.
trainingSetSize
Indicates the number of examples or instances included in a dataset used to train a model or system.
- 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_69e4e221bbbc819088a7559a46b7d4e7 |
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.