Triple
T17521236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MuJoCo environments |
E426682
|
entity |
| Predicate | basedOn |
P98
|
FINISHED |
| Object | MuJoCo physics engine |
—
|
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: MuJoCo physics engine | Statement: [MuJoCo environments, basedOn, MuJoCo physics engine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MuJoCo physics engine Context triple: [MuJoCo environments, basedOn, MuJoCo physics engine]
-
A.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning 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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
D.
MPE (Multi-Agent Particle Environments)
MPE (Multi-Agent Particle Environments) is a classic collection of lightweight 2D multi-agent reinforcement learning benchmark environments featuring simple particle-based agents and tasks like cooperation, competition, and communication.
-
E.
Farama Foundation
The Farama Foundation is an organization that develops and maintains open-source reinforcement learning tools and libraries for the research and engineering community.
- 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: MuJoCo physics engine Target entity description: MuJoCo physics engine is a high-performance, open-source physics simulator widely used in robotics and reinforcement learning research for accurate, efficient modeling of complex dynamical systems.
-
A.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning 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.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
D.
MPE (Multi-Agent Particle Environments)
MPE (Multi-Agent Particle Environments) is a classic collection of lightweight 2D multi-agent reinforcement learning benchmark environments featuring simple particle-based agents and tasks like cooperation, competition, and communication.
-
E.
Farama Foundation
The Farama Foundation is an organization that develops and maintains open-source reinforcement learning tools and libraries for the research and engineering community.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d2f79881909556894728e255ab |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.