Triple

T25366540
Position Surface form Disambiguated ID Type / Status
Subject TD(λ) E636114 entity
Predicate instanceOf P0 FINISHED
Object temporal-difference learning algorithm 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 algorithm
Context triple: [TD(λ), instanceOf, temporal-difference learning algorithm]
  • 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. 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 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.
  • D. policy gradient algorithm
    A policy gradient algorithm is a reinforcement learning method that directly optimizes a parameterized policy by estimating and following the gradient of expected cumulative reward with respect to the policy parameters.
  • E. 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.
  • 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_69e75a9b7cf481909f2dcdfb37d95ca7 completed April 21, 2026, 11:08 a.m.
Created at: April 21, 2026, 1:37 p.m.