Triple

T7874690
Position Surface form Disambiguated ID Type / Status
Subject Adam optimizer E182821 entity
Predicate instanceOf P0 FINISHED
Object stochastic gradient descent variant 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: stochastic gradient descent variant
Context triple: [Adam optimizer, instanceOf, stochastic gradient descent variant]
  • A. 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.
  • B. model-based reinforcement learning algorithm
    A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
  • C. 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.
  • D. actor-critic method
    An actor-critic method is a reinforcement learning approach that combines a policy model (actor) that selects actions with a value model (critic) that evaluates those actions to improve the policy.
  • E. hyperparameter optimization tool
    A hyperparameter optimization tool is a system that automatically searches, evaluates, and selects the best hyperparameter configurations to improve the performance of machine learning models.
  • 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_69ca828a17248190b46defe758bc5ad3 completed March 30, 2026, 2:02 p.m.
Created at: March 30, 2026, 4:56 p.m.