Triple

T32415273
Position Surface form Disambiguated ID Type / Status
Subject Azure Personalizer E828318 entity
Predicate instanceOf P0 FINISHED
Object reinforcement learning service C57938 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: reinforcement learning service
Context triple: [Azure Personalizer, instanceOf, reinforcement learning service]
  • A. reinforcement learning library
    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. 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.
  • C. model-based reinforcement learning algorithm
    A model-based reinforcement learning algorithm is a decision-making method that learns or uses an explicit model of the environment’s dynamics to plan and select actions that maximize long-term rewards.
  • D. value-based reinforcement learning method
    A value-based reinforcement learning method is an approach that learns a value function estimating expected future rewards for states or state-action pairs and derives a policy by selecting actions that maximize these estimated values.
  • E. 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.
  • F. None of above. chosen

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_69f34919f300819092b541c6277cd68a completed April 30, 2026, 12:20 p.m.
Created at: May 1, 2026, 12:54 a.m.