Triple

T1793155
Position Surface form Disambiguated ID Type / Status
Subject Atari deep Q-network E39543 entity
Predicate instanceOf P0 FINISHED
Object deep Q-network 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 Q-network
Context triple: [Atari deep Q-network, instanceOf, deep Q-network]
  • A. 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.
  • B. 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.
  • 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. dynamic game
    A dynamic game is a strategic interaction among multiple decision-makers that unfolds over time, where players’ choices at each stage can depend on past actions and information, influencing future payoffs and outcomes.
  • 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.