Triple
T18300656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AEC API |
E438348
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | PettingZoo AEC environments |
—
|
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: PettingZoo AEC environments | Statement: [AEC API, usedBy, PettingZoo AEC environments]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PettingZoo AEC environments Context triple: [AEC API, usedBy, PettingZoo AEC environments]
-
A.
PettingZoo
chosen
PettingZoo is a Python library that provides a standardized interface and tools for developing, running, and benchmarking multi-agent reinforcement learning environments.
-
B.
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.
-
C.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
D.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
E.
Minigrid
Minigrid is a lightweight, gridworld-based reinforcement learning environment suite commonly used for research on sample-efficient learning and generalization.
- 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_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.