Triple

T27762312
Position Surface form Disambiguated ID Type / Status
Subject Batch Normalization E701500 entity
Predicate instanceOf P0 FINISHED
Object regularization technique C23071 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: regularization technique
Context triple: [Batch Normalization, instanceOf, regularization technique]
  • A. neural network normalization technique chosen
    A neural network normalization technique is a method that rescales and shifts activations or inputs within a model to stabilize training, improve convergence, and enhance generalization.
  • B. parameter estimation technique
    A parameter estimation technique is a systematic method used to infer the numerical values of unknown model parameters from observed data, typically by optimizing a chosen error or likelihood criterion.
  • C. regularization scheme in quantum field theory
    A regularization scheme in quantum field theory is a systematic procedure for modifying divergent integrals or sums—typically by introducing an auxiliary parameter or cutoff—so that they become finite and mathematically well-defined while preserving as much of the theory’s symmetry and structure as possible.
  • 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. technique in analysis
    A technique in analysis is a systematic method or procedure used to examine, simplify, or solve mathematical problems involving limits, continuity, differentiation, integration, or related structures.
  • 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_69ef6a5193808190816eb7d0020b2d87 completed April 27, 2026, 1:53 p.m.
Created at: April 27, 2026, 4:28 p.m.