Triple

T18204426
Position Surface form Disambiguated ID Type / Status
Subject BART E435868 entity
Predicate instanceOf P0 FINISHED
Object sequence-to-sequence transformer model C25414 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: sequence-to-sequence transformer model
Context triple: [BART, instanceOf, sequence-to-sequence transformer model]
  • A. natural language processing model chosen
    A natural language processing model is a computational system designed to understand, interpret, generate, and manipulate human language in a meaningful way.
  • B. 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.
  • C. multimodal large language model family
    A multimodal large language model family is a group of related neural models that can jointly process and generate multiple data modalities—such as text, images, audio, or video—using shared architectures, training objectives, and parameterizations.
  • D. 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.
  • E. 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.
  • 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.