Triple
T18204572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DeBERTa |
E435870
|
entity |
| Predicate | usesPretrainingData |
P21227
|
FINISHED |
| Object | large-scale web text |
—
|
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: large-scale web text | Statement: [DeBERTa, usesPretrainingData, large-scale web text]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesPretrainingData Context triple: [DeBERTa, usesPretrainingData, large-scale web text]
-
A.
trainingDataIncludes
chosen
Indicates that one entity’s training dataset contains or incorporates the other entity as part of its data.
-
B.
supportsPretrainedModels
Indicates that an entity provides compatibility with or functionality for using pretrained models.
-
C.
usesTrainingStrategy
Indicates that one entity applies or follows a particular training strategy in carrying out its learning or optimization process.
-
D.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
-
E.
requiresFineTuningOf
Indicates that one entity needs the adjustment, calibration, or refinement of another entity in order to function correctly or optimally.
- 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.