Triple

T18204427
Position Surface form Disambiguated ID Type / Status
Subject BART E435868 entity
Predicate instanceOf P0 FINISHED
Object denoising autoencoder C4177 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: denoising autoencoder
Context triple: [BART, instanceOf, denoising autoencoder]
  • A. deep learning model chosen
    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.
  • B. 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.
  • C. 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.
  • D. deep learning library
    A deep learning library is a software framework that provides tools, abstractions, and optimized routines to design, train, and deploy neural network models.
  • E. deep learning framework
    A deep learning framework is a software library or platform that provides tools, abstractions, and optimized components to design, train, and deploy neural network models efficiently.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
Created at: April 10, 2026, 10:32 a.m.