Triple

T6042495
Position Surface form Disambiguated ID Type / Status
Subject RMSProp E134579 entity
Predicate instanceOf P0 FINISHED
Object gradient-based optimization method C6819 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: gradient-based optimization method
Context triple: [RMSProp, instanceOf, gradient-based optimization method]
  • A. 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.
  • B. 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.
  • C. 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.
  • 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. algorithm chosen
    An algorithm is a finite, well-defined sequence of computational steps or rules designed to solve a specific problem or perform a particular task.
  • 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_69c00876a69881908088a2626d3b2666 completed March 22, 2026, 3:19 p.m.
Created at: March 22, 2026, 4:08 p.m.