Triple

T15555794
Position Surface form Disambiguated ID Type / Status
Subject ResNet E370862 entity
Predicate instanceOf P0 FINISHED
Object deep 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: deep neural network architecture
Context triple: [ResNet, instanceOf, deep neural network architecture]
  • 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. neural network design method
    A neural network design method is a systematic approach for selecting, structuring, and configuring neural network architectures and training procedures to solve specific computational or learning tasks.
  • C. 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.
  • D. 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.
  • E. 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.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
Created at: April 10, 2026, 4:09 a.m.