Triple

T13625135
Position Surface form Disambiguated ID Type / Status
Subject Andrew Barto E325558 entity
Predicate authorOf P4244 FINISHED
Object "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.
 E1051241 NE FINISHED

How this triple was built (4 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: "Reinforcement Learning: An Introduction" | Statement: [Andrew Barto, authorOf, "Reinforcement Learning: An Introduction"]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: "Reinforcement Learning: An Introduction"
Context triple: [Andrew Barto, authorOf, "Reinforcement Learning: An Introduction"]
  • A. 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.
  • 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. 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. REINFORCE
    REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
  • E. Technical Committee on Robot Learning
    The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: "Reinforcement Learning: An Introduction"
Triple: [Andrew Barto, authorOf, "Reinforcement Learning: An Introduction"]
Generated description
"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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: "Reinforcement Learning: An Introduction"
Target entity description: "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.
  • A. 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.
  • 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. 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. REINFORCE
    REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
  • E. Technical Committee on Robot Learning
    The Technical Committee on Robot Learning is a specialized IEEE Robotics and Automation Society group that advances research and collaboration at the intersection of machine learning and robotics.
  • F. None of above. chosen

Provenance (5 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbbe9c72c88190be3d7a3f2e96afbc completed April 12, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77fa4c5fc8190bd791f181fce2aa1 completed May 3, 2026, 5:02 p.m.
NEDg Description generation batch_69f78070e95c819088982e26fe2d8e26 completed May 3, 2026, 5:05 p.m.
NED2 Entity disambiguation (via description) batch_69f78157b9cc8190a1855cb9715aa7d5 completed May 3, 2026, 5:09 p.m.
Created at: April 9, 2026, 9:50 p.m.