Triple

T6993316
Position Surface form Disambiguated ID Type / Status
Subject Proximal Policy Optimization E162136 entity
Predicate evaluationBenchmarks P23745 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: [Proximal Policy Optimization, evaluationBenchmarks, OpenAI Gym]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OpenAI Gym
Context triple: [Proximal Policy Optimization, evaluationBenchmarks, 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: evaluationBenchmarks
Context triple: [Proximal Policy Optimization, evaluationBenchmarks, OpenAI Gym]
  • A. evaluationBasis
    Indicates the criteria, standards, or reference framework used to judge, assess, or measure something in an evaluation process.
  • B. benchmarkFor chosen
    Indicates that one entity serves as a standard or reference point against which the performance, quality, or characteristics of another entity are measured or evaluated.
  • C. primaryBenchmarkProvider
    Indicates that one entity serves as the main or default source of benchmark data or performance standards for another entity.
  • D. benchmarkStatus
    Indicates the current evaluation state or outcome of a benchmark process applied to an entity or system.
  • E. evaluationFunction
    Indicates a relationship where a specific procedure or rule assigns a value or score to an input, typically to assess its quality, utility, or desirability.
  • 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_69c68856d7808190ab33ee914640281b completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dbc30fdc81909244d83c8178755c completed March 27, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a161f088190bbc3c4e2815fa929 completed March 28, 2026, 5:41 a.m.
PD Predicate disambiguation batch_69c6d7c4a18881908d267137daed828b completed March 27, 2026, 7:17 p.m.
Created at: March 27, 2026, 2:32 p.m.