Triple

T35689833
Position Surface form Disambiguated ID Type / Status
Subject Bayesian learning for neural networks E1031257 entity
Predicate instanceOf P0 FINISHED
Object neural network training approach C39344 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: neural network training approach
Context triple: [Bayesian learning for neural networks, instanceOf, neural network training approach]
  • A. 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.
  • B. machine learning paradigm chosen
    A machine learning paradigm is a conceptual framework that defines how models learn from data, including the assumptions, learning objectives, and training procedures that guide the development and application of algorithms.
  • C. 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.
  • D. adaptive learning rate method
    An adaptive learning rate method is an optimization technique that automatically adjusts the step size for each parameter during training based on past gradient information to improve convergence speed and stability.
  • E. neural network component
    A neural network component is a modular unit—such as a layer, activation function, or connection pattern—that processes and transforms input data as part of a larger neural architecture to enable learning and inference.
  • 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_69f76e0c73ec819080ab60a9e2f5f1f6 completed May 3, 2026, 3:47 p.m.
Created at: May 3, 2026, 4:05 p.m.