Triple

T17694169
Position Surface form Disambiguated ID Type / Status
Subject Hindsight Policy Gradients E441117 entity
Predicate introducedInPaper P513 FINISHED
Object Hindsight Policy Gradients — 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 Policy Gradients | Statement: [Hindsight Policy Gradients, introducedInPaper, Hindsight Policy Gradients]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hindsight Policy Gradients
Context triple: [Hindsight Policy Gradients, introducedInPaper, Hindsight Policy Gradients]
  • A. Hindsight Policy Gradients chosen
    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.
  • B. Hindsight Experience Replay
    Hindsight Experience Replay is a reinforcement learning technique that improves sample efficiency by reinterpreting failed attempts as successful experiences toward alternative goals.
  • C. Natural Policy Gradient
    Natural Policy Gradient is a reinforcement learning optimization method that improves policy gradient updates by accounting for the geometry of the parameter space using the Fisher information matrix, leading to more stable and efficient learning.
  • D. Proximal Policy Optimization
    Proximal Policy Optimization is a popular reinforcement learning algorithm that improves policy gradient methods by using clipped objective functions to achieve stable and efficient training.
  • 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_69d8b9e940b081908b862bb0e6e89b0d completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e4715485d88190b9b6f347ff85d7c7 completed April 19, 2026, 6:08 a.m.
Created at: April 10, 2026, 10:04 a.m.