Triple

T18300619
Position Surface form Disambiguated ID Type / Status
Subject AEC API E438348 entity
Predicate instanceOf P0 FINISHED
Object multi-agent reinforcement learning API C6080 CONCEPT FINISHED

How this triple was built (1 step)

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.

CD Concept disambiguation gpt-5-mini-2025-08-07 
Target class: multi-agent reinforcement learning API
Context triple: [AEC API, instanceOf, multi-agent reinforcement learning API]
  • A. reinforcement learning library chosen
    A reinforcement learning library is a software toolkit that provides algorithms, environments, and utilities to design, train, evaluate, and deploy agents that learn optimal behaviors through trial-and-error interactions with their environment.
  • B. reinforcement learning environment collection
    A reinforcement learning environment collection is an organized set of diverse, standardized simulation or task environments designed to train, evaluate, and benchmark reinforcement learning agents.
  • C. scalable RL architecture
    A scalable RL architecture is a modular, distributed system design that efficiently trains and serves reinforcement learning agents across large state-action spaces, high data volumes, and many concurrent tasks or environments.
  • D. Monte Carlo reinforcement learning algorithm
    A Monte Carlo reinforcement learning algorithm is a method that learns optimal policies by estimating value functions from complete, sampled episodes of experience without requiring a model of the environment’s dynamics.
  • E. actor-critic method
    An actor-critic method is a reinforcement learning approach that combines a policy model (actor) that selects actions with a value model (critic) that evaluates those actions to improve the policy.
  • F. None of above.

Provenance (1 batch)

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.
Created at: April 10, 2026, 10:35 a.m.