Triple

T18629539
Position Surface form Disambiguated ID Type / Status
Subject Q-learning E455376 entity
Predicate instanceOf P0 FINISHED
Object temporal-difference learning method C9067 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: temporal-difference learning method
Context triple: [Q-learning, instanceOf, temporal-difference learning method]
  • A. Monte Carlo reinforcement learning algorithm
    A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
  • B. value-based reinforcement learning method chosen
    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. 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.
  • 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. 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.
  • 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_69d8d38cc7948190a55ea64e5638994e completed April 10, 2026, 10:40 a.m.
Created at: April 10, 2026, 11:46 a.m.