Triple

T25366542
Position Surface form Disambiguated ID Type / Status
Subject TD(λ) E636114 entity
Predicate instanceOf P0 FINISHED
Object value function 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: value function learning method
Context triple: [TD(λ), instanceOf, value function learning method]
  • A. 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.
  • B. 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.
  • 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. 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.
  • 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.