Triple

T4293723
Position Surface form Disambiguated ID Type / Status
Subject Stable Baselines E99657 entity
Predicate supportsEnvironmentInterface P11686 FINISHED
Object OpenAI Gym E17413 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: OpenAI Gym | Statement: [Stable Baselines, supportsEnvironmentInterface, OpenAI Gym]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OpenAI Gym
Context triple: [Stable Baselines, supportsEnvironmentInterface, OpenAI Gym]
  • A. OpenAI Gym chosen
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • B. OpenAI Baselines
    OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
  • C. MuJoCo environments
    MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
  • D. Arcade Learning Environment
    Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
  • E. TF-Agents
    TF-Agents is an open-source library built on TensorFlow that provides modular components and tools for developing, training, and evaluating reinforcement learning algorithms.
  • 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: supportsEnvironmentInterface
Context triple: [Stable Baselines, supportsEnvironmentInterface, OpenAI Gym]
  • A. supportsEnvironment
    Indicates that one entity provides the necessary conditions, compatibility, or resources for another entity to operate or exist within a particular environment.
  • B. hasEnvironmentType
    Indicates that an entity is associated with or occurs within a specific type or category of environment.
  • C. supportsConfigurationFromEnv
    Indicates that a component can obtain and apply its configuration settings directly from environment variables.
  • D. usesInterface chosen
    Indicates that one entity interacts with or operates another entity through a specified interface or set of interface methods.
  • E. supportsImplementationOf
    Indicates that one entity provides the necessary resources, framework, or assistance for another entity to be carried out, realized, or put into practice.
  • 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_69b3455175088190aa79c6e03b86647e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35082228081908504e3fd7c4ca1e8 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5c73d47448190a844bc13eae84a54 completed March 14, 2026, 8:38 p.m.
PD Predicate disambiguation batch_69b347fe55a88190b77bab0c0f38e1aa completed March 12, 2026, 11:10 p.m.
Created at: March 12, 2026, 11:08 p.m.