Triple

T8737802
Position Surface form Disambiguated ID Type / Status
Subject WaveNet E207427 entity
Predicate instanceOf P0 FINISHED
Object autoregressive neural network C24489 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: autoregressive neural network
Context triple: [WaveNet, instanceOf, autoregressive neural network]
  • A. recurrent artificial neural network
    A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
  • B. autoregressive neural vocoder chosen
    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.
  • C. 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.
  • D. deep learning model
    A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
  • E. neural network API
    A neural network API is an interface that allows developers to build, configure, train, and deploy neural network models programmatically without managing low-level implementation details.
  • 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_69ca835a03a081909d4d4cd01a18c9fb completed March 30, 2026, 2:06 p.m.
Created at: March 30, 2026, 6:38 p.m.