Triple
T18300863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VectorEnv interface |
E438353
|
entity |
| Predicate | implementedBy |
P172
|
FINISHED |
| Object | AsyncVectorEnv |
—
|
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: AsyncVectorEnv | Statement: [VectorEnv interface, implementedBy, AsyncVectorEnv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AsyncVectorEnv Context triple: [VectorEnv interface, implementedBy, AsyncVectorEnv]
-
A.
VectorEnv interface
chosen
The VectorEnv interface is a Gymnasium API for running multiple reinforcement learning environments in parallel as a single batched environment to enable more efficient data collection and training.
-
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.
Ant-v2
Ant-v2 is a MuJoCo-based continuous control benchmark task in which a four-legged ant-like robot must learn to move efficiently through reinforcement learning.
-
D.
concurrent_vector
concurrent_vector is a thread-safe, growable array container in Intel Threading Building Blocks designed for efficient concurrent access and modification by multiple threads.
-
E.
MADDPG
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is a reinforcement learning algorithm that extends DDPG to multi-agent settings by using centralized training with decentralized execution for cooperative and competitive tasks.
- 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.