Triple

T4470288
Position Surface form Disambiguated ID Type / Status
Subject ACKTR E98477 entity
Predicate abbreviationOf P590 FINISHED
Object Actor-Critic using Kronecker-Factored Trust Region E441103 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: Actor-Critic using Kronecker-Factored Trust Region | Statement: [ACKTR, abbreviationOf, Actor-Critic using Kronecker-Factored Trust Region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Actor-Critic using Kronecker-Factored Trust Region
Context triple: [ACKTR, abbreviationOf, Actor-Critic using Kronecker-Factored Trust Region]
  • A. Actor-Critic using Kronecker-Factored Trust Region chosen
    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.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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 (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_69b3454b4ae481908967426dd37284d6 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b356b6a1f48190a39f5411648c40ff completed March 13, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6377154bc819099362e8b28698dbe completed March 15, 2026, 4:37 a.m.
Created at: March 12, 2026, 11:34 p.m.