Triple

T19771969
Position Surface form Disambiguated ID Type / Status
Subject Cascade-Correlation learning architecture E474908 entity
Predicate instanceOf P0 FINISHED
Object constructive neural network algorithm C28997 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: constructive neural network algorithm
Context triple: [Cascade-Correlation learning architecture, instanceOf, constructive neural network algorithm]
  • A. neural network design method chosen
    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. 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.
  • C. neural networks conference
    A neural networks conference is a professional gathering where researchers, practitioners, and industry experts present, discuss, and collaborate on the latest advances, applications, and theories in neural network and deep learning technologies.
  • D. 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.
  • E. 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.
  • 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_69d8e51a43a08190956bc6df13c91a77 completed April 10, 2026, 11:55 a.m.
Created at: April 10, 2026, 1:48 p.m.