Triple

T36491583
Position Surface form Disambiguated ID Type / Status
Subject Matching Networks E899063 entity
Predicate instanceOf P0 FINISHED
Object few-shot learning method 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: few-shot learning method
Context triple: [Matching Networks, instanceOf, few-shot learning method]
  • A. 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.
  • B. unsupervised learning method
    An unsupervised learning method is a type of machine learning approach that discovers patterns, structures, or groupings in unlabeled data without predefined output targets.
  • C. 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.
  • D. 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.
  • E. value-based reinforcement learning method
    A value-based reinforcement learning method is an approach that learns a value function estimating expected future rewards for states or state-action pairs and derives a policy by selecting actions that maximize these estimated values.
  • 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_69f76e5ad4588190bdbce60c52fbb785 completed May 3, 2026, 3:48 p.m.
Created at: May 3, 2026, 4:10 p.m.