Triple

T8577271
Position Surface form Disambiguated ID Type / Status
Subject Parallel WaveNet E203077 entity
Predicate title P38 FINISHED
Object Parallel WaveNet: Fast High-Fidelity Speech Synthesis E203077 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: Parallel WaveNet: Fast High-Fidelity Speech Synthesis | Statement: [Parallel WaveNet, title, Parallel WaveNet: Fast High-Fidelity Speech Synthesis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Context triple: [Parallel WaveNet, title, Parallel WaveNet: Fast High-Fidelity Speech Synthesis]
  • A. Parallel WaveNet chosen
    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.
  • B. WaveNet
    WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
  • C. 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.
  • D. WaveGlow
    WaveGlow is a flow-based generative neural network model for fast, high-quality text-to-speech audio synthesis.
  • E. Wav2Vec2
    Wav2Vec2 is a self-supervised deep learning model for automatic speech recognition that learns powerful audio representations directly from raw waveforms.
  • 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_69ca8328ebe481909a8c038fa79959b4 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbea97787481909ebbaa45f59cbdaa completed March 31, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea89550f481908a7ed45303b71731 completed April 2, 2026, 5:34 p.m.
Created at: March 30, 2026, 6:22 p.m.