Triple

T805161
Position Surface form Disambiguated ID Type / Status
Subject OpenAI Gym E17413 entity
Predicate supports P516 FINISHED
Object OpenAI Baselines E17813 NE FINISHED

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: OpenAI Baselines | Statement: [OpenAI Gym, supports, OpenAI Baselines]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: OpenAI Baselines
Context triple: [OpenAI Gym, supports, OpenAI Baselines]
  • A. OpenAI Baselines chosen
    OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
  • B. OpenAI Gym
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • C. 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.
  • D. 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.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a4937ae8a08190b5084a03d532b30e completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aabff3d88190bec4299fa0d87df0 completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d83cf448190a2205cd777386833 completed March 3, 2026, 11:23 p.m.
Created at: March 1, 2026, 7:38 p.m.