Triple

T17521127
Position Surface form Disambiguated ID Type / Status
Subject Soft Actor-Critic E426679 entity
Predicate commonlyEvaluatedOn P11801 FINISHED
Object MuJoCo benchmarks — NE NERFINISHED

How this triple was built (2 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: MuJoCo benchmarks | Statement: [Soft Actor-Critic, commonlyEvaluatedOn, MuJoCo benchmarks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MuJoCo benchmarks
Context triple: [Soft Actor-Critic, commonlyEvaluatedOn, MuJoCo benchmarks]
  • A. MuJoCo environments chosen
    MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
  • 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. OpenAI Gym
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • D. Stable Baselines
    Stable Baselines is a popular Python library that provides reliable, well-tested implementations of reinforcement learning algorithms built on top of OpenAI Baselines.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d23cf08190925510344fa36f57 completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.