Triple

T4470520
Position Surface form Disambiguated ID Type / Status
Subject Hindsight Experience Replay E98482 entity
Predicate instanceOf P0 FINISHED
Object experience replay method C15712 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: experience replay method
Context triple: [Hindsight Experience Replay, instanceOf, experience replay method]
  • A. 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.
  • B. 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.
  • 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. recurrent artificial neural network
    A recurrent artificial neural network is a type of neural network where connections form directed cycles, allowing information to persist over time and enabling the modeling of sequential or temporal data.
  • E. checkpoint
    A checkpoint is a designated point in a process, journey, or system where progress is recorded, conditions are verified, or control actions are applied before continuation.
  • 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_69b3454b4ae481908967426dd37284d6 completed March 12, 2026, 10:59 p.m.
Created at: March 12, 2026, 11:34 p.m.