Triple

T17792754
Position Surface form Disambiguated ID Type / Status
Subject Peter Welinder E444208 entity
Predicate notableWork P4 FINISHED
Object Hindsight Experience Replay in deep reinforcement learning — 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: Hindsight Experience Replay in deep reinforcement learning | Statement: [Peter Welinder, notableWork, Hindsight Experience Replay in deep reinforcement learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hindsight Experience Replay in deep reinforcement learning
Context triple: [Peter Welinder, notableWork, Hindsight Experience Replay in deep reinforcement learning]
  • A. Hindsight Experience Replay chosen
    Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.
  • B. Hindsight Policy Gradients
    Hindsight Policy Gradients is a reinforcement learning algorithm that extends policy gradient methods by retrospectively reinterpreting failed trajectories as successes for alternative goals, improving learning efficiency in sparse-reward environments.
  • C. Prioritized Experience Replay DQN
    Prioritized Experience Replay DQN is a variant of the Deep Q-Network algorithm that improves learning efficiency by sampling more informative experiences with higher priority from the replay buffer.
  • D. Deep Q-Learning
    Deep Q-Learning is a reinforcement learning algorithm that uses deep neural networks to approximate Q-values, enabling agents to learn effective policies directly from high-dimensional inputs like raw images.
  • E. Generalized Advantage Estimation
    Generalized Advantage Estimation is a reinforcement learning technique that reduces variance and improves sample efficiency in policy gradient methods by cleverly estimating the advantage function over multiple time scales.
  • 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_69d8b9efe370819095cd219b143ae727 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4879859408190875835bd255e1185 completed April 19, 2026, 7:43 a.m.
Created at: April 10, 2026, 10:13 a.m.