Triple

T1180398
Position Surface form Disambiguated ID Type / Status
Subject deep feedforward networks E25122 entity
Predicate instanceOf P0 FINISHED
Object artificial neural network architecture 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: artificial neural network architecture
Context triple: [deep feedforward networks, instanceOf, artificial neural network architecture]
  • A. network architecture
    A network architecture is the structured design and organization of hardware, software, protocols, and communication paths that define how data flows and services are delivered within a computer network.
  • B. 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.
  • C. 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.
  • D. 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.
  • E. machine learning framework
    A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning 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_69a494267b4c819088c97a59182bf56a completed March 1, 2026, 7:31 p.m.
Created at: March 1, 2026, 7:45 p.m.