Triple

T18205246
Position Surface form Disambiguated ID Type / Status
Subject VisionEncoderDecoderModel E435885 entity
Predicate instanceOf P0 FINISHED
Object encoder-decoder model 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: encoder-decoder model
Context triple: [VisionEncoderDecoderModel, instanceOf, encoder-decoder model]
  • 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. large-scale model
    A large-scale model is a computational model, often in machine learning or simulation, that operates with vast numbers of parameters or variables to capture complex patterns or behaviors across extensive datasets or systems.
  • C. text-to-speech model
    A text-to-speech model is a system that converts written text into natural-sounding spoken audio using linguistic analysis and speech synthesis techniques.
  • D. image captioning model
    An image captioning model is a system that automatically generates descriptive natural language sentences that explain the content of an input image.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
Created at: April 10, 2026, 10:32 a.m.