Triple

T13625136
Position Surface form Disambiguated ID Type / Status
Subject Andrew Barto E325558 entity
Predicate authorOf P4244 FINISHED
Object "Reinforcement Learning: An Introduction" second edition E1051241 NE FINISHED

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: "Reinforcement Learning: An Introduction" second edition | Statement: [Andrew Barto, authorOf, "Reinforcement Learning: An Introduction" second edition]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: "Reinforcement Learning: An Introduction" second edition
Context triple: [Andrew Barto, authorOf, "Reinforcement Learning: An Introduction" second edition]
  • A. "Reinforcement Learning: An Introduction" chosen
    "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. Reinforcement Learning Lifetime Achievement-style recognitions
    Reinforcement Learning Lifetime Achievement-style recognitions are honors given to pioneers in reinforcement learning, such as Andrew Barto, for their foundational and long-term contributions to the field.
  • C. 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.
  • D. 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.
  • E. REINFORCE
    REINFORCE is a classic Monte Carlo policy gradient algorithm in reinforcement learning that optimizes stochastic policies by estimating gradients from sampled returns.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69f78aeb591481909d39675a543a8b51 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:50 p.m.