Triple

T17521203
Position Surface form Disambiguated ID Type / Status
Subject REINFORCE E426681 entity
Predicate introducedInPaper P513 FINISHED
Object Simple Statistical Gradient-Following Algorithms for Connectionist 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: Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning | Statement: [REINFORCE, introducedInPaper, Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
Context triple: [REINFORCE, introducedInPaper, Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning]
  • A. "Reinforcement Learning: An Introduction"
    "Reinforcement Learning: An Introduction" is a foundational textbook that systematically presents the core concepts, algorithms, and theory of reinforcement learning in an accessible and widely used form.
  • B. 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.
  • C. Q-learning
    Q-learning is a model-free reinforcement learning algorithm that learns an action-value function to optimize decision-making by estimating the expected cumulative reward for each state-action pair.
  • D. REINFORCE chosen
    REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
  • E. 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.
  • 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_69d889de677081909b22d2657b1f0292 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e452d2f79881909556894728e255ab completed April 19, 2026, 3:58 a.m.
Created at: April 10, 2026, 5:49 a.m.