Triple
T18205239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | HuBERT |
E435884
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | wav2vec 2.0 |
—
|
NE NERFINISHED |
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: wav2vec 2.0 | Statement: [HuBERT, relatedTo, wav2vec 2.0]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: wav2vec 2.0 Context triple: [HuBERT, relatedTo, wav2vec 2.0]
-
A.
Wav2Vec2
chosen
Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
-
B.
WavLM
WavLM is a self-supervised speech representation model developed by Microsoft that extends wav2vec-style architectures to better handle noisy, multi-speaker, and diverse speech scenarios for tasks like recognition and speaker diarization.
-
C.
HuBERT
HuBERT is a self-supervised speech representation learning model that learns powerful audio features from unlabeled speech for tasks like automatic speech recognition and audio classification.
-
D.
WaveRNN
WaveRNN is a neural network-based audio waveform generator designed as a more efficient, real-time alternative to earlier autoregressive models for tasks like text-to-speech synthesis.
-
E.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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. |
Created at: April 10, 2026, 10:32 a.m.