Triple

T6993290
Position Surface form Disambiguated ID Type / Status
Subject Proximal Policy Optimization E162136 entity
Predicate introducedInPaper P513 FINISHED
Object Proximal Policy Optimization Algorithms E162136 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: Proximal Policy Optimization Algorithms | Statement: [Proximal Policy Optimization, introducedInPaper, Proximal Policy Optimization Algorithms]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Proximal Policy Optimization Algorithms
Context triple: [Proximal Policy Optimization, introducedInPaper, Proximal Policy Optimization Algorithms]
  • A. Proximal Policy Optimization chosen
    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.
  • B. 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.
  • C. 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.
  • D. Asynchronous Advantage Actor-Critic
    Asynchronous Advantage Actor-Critic is a deep reinforcement learning algorithm that trains multiple parallel agents to learn both policy and value functions efficiently and stably.
  • E. Actor-Critic using Kronecker-Factored Trust Region
    Actor-Critic using Kronecker-Factored Trust Region (ACKTR) is a reinforcement learning algorithm that improves sample efficiency and stability by applying Kronecker-factored approximate curvature to natural gradient updates in actor-critic methods.
  • 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_69c68856d7808190ab33ee914640281b completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dbc30fdc81909244d83c8178755c completed March 27, 2026, 7:34 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a161f088190bbc3c4e2815fa929 completed March 28, 2026, 5:41 a.m.
Created at: March 27, 2026, 2:32 p.m.