Triple

T19771973
Position Surface form Disambiguated ID Type / Status
Subject Cascade-Correlation learning architecture E474908 entity
Predicate describedIn P519 FINISHED
Object The Cascade-Correlation Learning Architecture — NE NERFINISHED

How this triple was built (2 steps)

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.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: The Cascade-Correlation Learning Architecture | Statement: [Cascade-Correlation learning architecture, describedIn, The Cascade-Correlation Learning Architecture]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: The Cascade-Correlation Learning Architecture
Context triple: [Cascade-Correlation learning architecture, describedIn, The Cascade-Correlation Learning Architecture]
  • A. Cascade-Correlation learning architecture chosen
    Cascade-Correlation learning architecture is a neural network training method that incrementally builds its own topology by adding new hidden units during learning to improve performance.
  • B. Hopfield networks
    Hopfield networks are recurrent artificial neural networks that serve as content-addressable memory systems, storing patterns as stable states and retrieving them through dynamics that minimize an energy function.
  • C. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • D. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • E. Bayesian learning for neural networks
    Bayesian learning for neural networks is an approach that applies Bayesian inference to neural network models, treating their weights as probability distributions to improve uncertainty estimation and generalization.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

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.
NER Named-entity recognition batch_69e6535ce4d08190a1dfca2df95a8631 completed April 20, 2026, 4:25 p.m.
Created at: April 10, 2026, 1:48 p.m.