Triple

T18300779
Position Surface form Disambiguated ID Type / Status
Subject Farama Foundation E438351 entity
Predicate product P490 FINISHED
Object RL-Baselines3-Zoo — NE NERFINISHED

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: RL-Baselines3-Zoo | Statement: [Farama Foundation, product, RL-Baselines3-Zoo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: RL-Baselines3-Zoo
Context triple: [Farama Foundation, product, RL-Baselines3-Zoo]
  • A. 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.
  • 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. 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.
  • D. ChainerRL
    ChainerRL is a reinforcement learning library built on top of the Chainer deep learning framework, providing tools and algorithms for training and evaluating RL agents.
  • E. OpenAI Gym
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: RL-Baselines3-Zoo
Target entity description: RL-Baselines3-Zoo is an open-source collection of training scripts, hyperparameter presets, and tools built around Stable-Baselines3 to benchmark and reproduce reinforcement learning experiments across many environments.
  • A. 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.
  • 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. 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.
  • D. ChainerRL
    ChainerRL is a reinforcement learning library built on top of the Chainer deep learning framework, providing tools and algorithms for training and evaluating RL agents.
  • E. OpenAI Gym
    OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
  • F. None of above. chosen

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_69d8b915e3e881909125d760c15d0c29 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e5017f63dc819083a675d570620f2f completed April 19, 2026, 4:23 p.m.
Created at: April 10, 2026, 10:35 a.m.