Triple

T34302522
Position Surface form Disambiguated ID Type / Status
Subject forward-backward algorithm E880218 entity
Predicate instanceOf P0 FINISHED
Object probabilistic graphical model algorithm 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: probabilistic graphical model algorithm
Context triple: [forward-backward algorithm, instanceOf, probabilistic graphical model algorithm]
  • 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. 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. probabilistic robotics method
    A probabilistic robotics method is an approach that models robot perception, state estimation, and decision-making using probability theory to explicitly handle uncertainty in sensing and action.
  • D. 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.
  • 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_69f349b79f6c81909cb468c92c39c74d completed April 30, 2026, 12:23 p.m.
Created at: May 1, 2026, 1:57 a.m.