Triple
T8483165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WaveRNN |
E200566
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | dual softmax WaveRNN |
E200566
|
NE 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: dual softmax WaveRNN | Statement: [WaveRNN, hasVariant, dual softmax WaveRNN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: dual softmax WaveRNN Context triple: [WaveRNN, hasVariant, dual softmax WaveRNN]
-
A.
WaveRNN
chosen
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.
-
B.
WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
C.
Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
-
D.
Wav2Vec2
Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
-
E.
WaveGlow
WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca831b17988190a1f3f3413d57b820 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe53845e881909eeb32863c7aa942 |
completed | March 31, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce3a348a8481908a72c7ac15605022 |
completed | April 2, 2026, 9:43 a.m. |
Created at: March 30, 2026, 6:12 p.m.