Triple

T2373681
Position Surface form Disambiguated ID Type / Status
Subject Quoc V. Le E46144 entity
Predicate coAuthorOf P2389 FINISHED
Object Neural Architecture Search with Reinforcement Learning E260047 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: Neural Architecture Search with Reinforcement Learning | Statement: [Quoc V. Le, coAuthorOf, Neural Architecture Search with Reinforcement Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Neural Architecture Search with Reinforcement Learning
Context triple: [Quoc V. Le, coAuthorOf, Neural Architecture Search with Reinforcement Learning]
  • A. Neural Architecture Search chosen
    Neural Architecture Search is an automated machine learning technique that uses algorithms to design and optimize neural network architectures without extensive human intervention.
  • B. Adam: A Method for Stochastic Optimization
    "Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
  • 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. AutoML: A Survey of the State-of-the-Art
    "AutoML: A Survey of the State-of-the-Art" is a comprehensive academic survey paper that reviews and synthesizes methods, tools, and challenges in automated machine learning, including model selection, hyperparameter optimization, and neural architecture search.
  • E. Atari deep Q-network
    The Atari deep Q-network is a pioneering deep reinforcement learning system that learned to play a wide range of Atari 2600 video games directly from raw pixels at human-level or better performance.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc791c4688190a4b8f0e540e84eb4 completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3ca7ef88190902e1c8429694a82 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:56 p.m.