Triple
T4470395
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PPO2 |
E98479
|
entity |
| Predicate | commonlyUsedWith |
P3100
|
FINISHED |
| Object | OpenAI Gym environments |
E17413
|
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 Gym environments | Statement: [PPO2, commonlyUsedWith, OpenAI Gym environments]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: OpenAI Gym environments Context triple: [PPO2, commonlyUsedWith, OpenAI Gym environments]
-
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.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
C.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for 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.
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_69b3454b4ae481908967426dd37284d6 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356b6a1f48190a39f5411648c40ff |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b66b31c9488190bc24f802fff24073 |
completed | March 15, 2026, 8:17 a.m. |
Created at: March 12, 2026, 11:34 p.m.