Triple

T1793153
Position Surface form Disambiguated ID Type / Status
Subject Atari deep Q-network E39543 entity
Predicate instanceOf P0 FINISHED
Object deep reinforcement learning algorithm C4177 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: deep reinforcement learning algorithm
Context triple: [Atari deep Q-network, instanceOf, deep 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. deep learning model chosen
    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.
  • C. deep learning library
    A deep learning library is a software framework that provides tools, abstractions, and optimized routines to design, train, and deploy neural network models.
  • D. deep learning framework
    A deep learning framework is a software library or platform that provides tools, abstractions, and optimized components to design, train, and deploy neural network models efficiently.
  • E. 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.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
Created at: March 4, 2026, 7:32 p.m.