Triple

T29108300
Position Surface form Disambiguated ID Type / Status
Subject Connectionist Temporal Classification E736823 entity
Predicate instanceOf P0 FINISHED
Object neural network training algorithm C19814 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 algorithm
Context triple: [Connectionist Temporal Classification, instanceOf, neural network training algorithm]
  • 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. 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. adaptive learning rate method chosen
    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.
  • D. 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.
  • 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_69f077ec765c81909474c88bcc8bab43 completed April 28, 2026, 9:03 a.m.
Created at: April 28, 2026, 11:17 a.m.