Triple

T1961018
Position Surface form Disambiguated ID Type / Status
Subject MuZero E42386 entity
Predicate instanceOf P0 FINISHED
Object model-based reinforcement learning algorithm C8171 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: model-based reinforcement learning algorithm
Context triple: [MuZero, instanceOf, model-based reinforcement learning algorithm]
  • A. reinforcement learning library
    A reinforcement learning library is a software toolkit that provides algorithms, environments, and utilities to design, train, evaluate, and deploy agents that learn optimal behaviors through trial-and-error interactions with their environment.
  • B. machine learning framework
    A machine learning framework is a software library or platform that provides tools, abstractions, and workflows to design, train, evaluate, and deploy machine learning models efficiently.
  • C. deep learning model
    A deep learning model is a computational architecture composed of multiple layers of interconnected processing units (neurons) that automatically learn hierarchical representations from data to perform tasks such as classification, prediction, or generation.
  • D. machine learning book
    A machine learning book is a structured, written resource that explains the theories, algorithms, and practical applications of machine learning to help readers understand and apply data-driven modeling techniques.
  • E. machine learning library
    A machine learning library is a collection of tools, algorithms, and interfaces that simplifies building, training, evaluating, and deploying machine learning models.
  • F. None of above. chosen

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_69a8870eea088190a38781990812a9bc completed March 4, 2026, 7:25 p.m.
Created at: March 4, 2026, 7:36 p.m.