Triple
T8737782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aaron van den Oord |
E207427
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | WaveNet: A Generative Model for Raw Audio |
E39544
|
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: WaveNet: A Generative Model for Raw Audio | Statement: [Aaron van den Oord, notableWork, WaveNet: A Generative Model for Raw Audio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WaveNet: A Generative Model for Raw Audio Context triple: [Aaron van den Oord, notableWork, WaveNet: A Generative Model for Raw Audio]
-
A.
WaveNet
chosen
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
-
B.
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.
-
C.
Tacotron
Tacotron is a neural network-based text-to-speech system that generates natural-sounding speech by predicting mel-spectrograms from text, often used in conjunction with neural vocoders like Parallel WaveNet.
-
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.
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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d45c96081909aa8509064ff3a04 |
completed | March 31, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf42c9140081909f9c10560757c860 |
completed | April 3, 2026, 4:32 a.m. |
Created at: March 30, 2026, 6:38 p.m.