Triple
T18205180
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wav2Vec2 |
E435883
|
entity |
| Predicate | fineTuningDataType |
P21226
|
FINISHED |
| Object | labeled speech with transcripts |
—
|
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: labeled speech with transcripts | Statement: [Wav2Vec2, fineTuningDataType, labeled speech with transcripts]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fineTuningDataType Context triple: [Wav2Vec2, fineTuningDataType, labeled speech with transcripts]
-
A.
trainingDataType
chosen
Indicates the type or category of data used for training a model, system, or process.
-
B.
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.
-
C.
requiresFineTuningOf
Indicates that one entity needs the adjustment, calibration, or refinement of another entity in order to function correctly or optimally.
-
D.
equipmentTypeTrainedOn
Indicates the type of equipment on which an entity has received training or is qualified to operate.
-
E.
acceleratorType
Indicates the kind or category of accelerator associated with or used by an 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.