Triple

T29418499
Position Surface form Disambiguated ID Type / Status
Subject Tacotron E746091 entity
Predicate instanceOf P0 FINISHED
Object end-to-end TTS model C9068 CONCEPT FINISHED

How this triple was built (1 step)

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.

CD Concept disambiguation gpt-5-mini-2025-08-07 
Target class: end-to-end TTS model
Context triple: [Tacotron, instanceOf, end-to-end TTS model]
  • A. text-to-speech model chosen
    A text-to-speech model is a system that converts written text into natural-sounding spoken audio using linguistic analysis and speech synthesis techniques.
  • B. speech foundation model
    A speech foundation model is a large-scale, pre-trained neural network designed to understand, generate, and transform spoken language across diverse tasks, languages, and acoustic conditions.
  • C. self-supervised speech representation learning model
    A self-supervised speech representation learning model is a neural network that learns meaningful audio and speech feature representations directly from large amounts of unlabeled speech data by solving pretext tasks such as masked prediction or contrastive learning.
  • D. autoregressive-free vocoder
    An autoregressive-free vocoder is a neural audio synthesis model that generates high-quality speech or sound waveforms in parallel, without relying on step-by-step autoregressive prediction.
  • E. autoregressive neural vocoder
    An autoregressive neural vocoder is a generative model that synthesizes high-quality audio waveforms sample-by-sample by predicting each new sample conditioned on previously generated samples and acoustic features.
  • F. None of above.

Provenance (1 batch)

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_69f0a79f6d5c8190a350baed0157e06f completed April 28, 2026, 12:27 p.m.
Created at: April 28, 2026, 3:03 p.m.