Triple

T17999776
Position Surface form Disambiguated ID Type / Status
Subject Adrià Puigdomènech Badia E430595 entity
Predicate knownFor P22 FINISHED
Object Asynchronous Advantage Actor-Critic — 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: Asynchronous Advantage Actor-Critic | Statement: [Adrià Puigdomènech Badia, knownFor, Asynchronous Advantage Actor-Critic]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Asynchronous Advantage Actor-Critic
Context triple: [Adrià Puigdomènech Badia, knownFor, Asynchronous Advantage Actor-Critic]
  • A. Asynchronous Advantage Actor-Critic chosen
    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.
  • B. Soft Actor-Critic
    Soft Actor-Critic is a model-free deep reinforcement learning algorithm that combines off-policy learning with entropy maximization to achieve stable and sample-efficient continuous control.
  • C. 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.
  • D. Asynchronous Methods for Deep Reinforcement Learning
    "Asynchronous Methods for Deep Reinforcement Learning" is a 2016 DeepMind paper that introduced asynchronous parallel training techniques for deep reinforcement learning, most notably the A3C algorithm, enabling more stable and efficient learning without specialized hardware.
  • E. 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.
  • 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_69d8b90364248190a37381adea932f42 completed April 10, 2026, 8:46 a.m.
NER Named-entity recognition batch_69e4b3e75e908190a6ff6a3ec6069ff5 completed April 19, 2026, 10:52 a.m.
Created at: April 10, 2026, 10:23 a.m.