Triple
T18204893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | mBART |
E435877
|
entity |
| Predicate | pretrainedOn |
P130221
|
FINISHED |
| Object | large multilingual corpora |
—
|
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 multilingual corpora | Statement: [mBART, pretrainedOn, large multilingual corpora]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pretrainedOn Context triple: [mBART, pretrainedOn, large multilingual corpora]
-
A.
supportsPretrainedModels
Indicates that an entity provides compatibility with or functionality for using pretrained models.
-
B.
trainerModel
Indicates that one entity serves as the trainer or training source for a model entity.
-
C.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
-
D.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
-
E.
pretrainingRole
Indicates the role or function an entity serves specifically during a pretraining phase or process.
- 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.