Triple

T1793184
Position Surface form Disambiguated ID Type / Status
Subject Atari deep Q-network E39543 entity
Predicate environmentFramework P9140 FINISHED
Object Arcade Learning Environment E95193 NE FINISHED

How this triple was built (3 steps)

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.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Arcade Learning Environment | Statement: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arcade Learning Environment
Context triple: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
  • A. Arcade Learning Environment chosen
    Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
  • B. Atari deep Q-network
    The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
  • C. OpenAI Baselines
    OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
  • D. OpenAI Gym
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • E. MuZero
    MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: environmentFramework
Context triple: [Atari deep Q-network, environmentFramework, Arcade Learning Environment]
  • A. environmentType
    Indicates the kind or category of environment associated with an entity or situation.
  • B. supportsEnvironment
    Indicates that one entity provides the necessary conditions, compatibility, or resources for another entity to operate or exist within a particular environment.
  • C. frameworkFor chosen
    Indicates that one entity serves as a supporting structure, system, or basis that organizes, guides, or enables the development or functioning of another entity.
  • D. environmentalMedium
    Indicates the environmental context or medium (such as air, water, or soil) through which a substance, effect, or process occurs or is present.
  • E. primaryOperatingEnvironment
    Indicates the main environment, platform, or context in which an entity is primarily designed to operate or function.
  • F. None of above.

Provenance (4 batches)

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.
NER Named-entity recognition batch_69ab61b6ea188190aab9fb839bf1e367 completed March 6, 2026, 11:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69adb5d26afc81909675064289d3a5b8 completed March 8, 2026, 5:45 p.m.
PD Predicate disambiguation batch_69aa61d2f7a8819090301f92d3e358c7 completed March 6, 2026, 5:10 a.m.
Created at: March 4, 2026, 7:32 p.m.