Triple

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).

Phrase: Mastering Atari, Go, Chess and Shogi by planning with a learned model | Statement: [Ioannis Antonoglou, coAuthorOf, Mastering Atari, Go, Chess and Shogi by planning with a learned model]

Target entity: Mastering Atari, Go, Chess and Shogi by planning with a learned model
Context triple: [Ioannis Antonoglou, coAuthorOf, Mastering Atari, Go, Chess and Shogi by planning with a learned model]

• A. Monte Carlo tree search
  Monte Carlo tree search is a heuristic search algorithm that uses random sampling of game states to build and explore a search tree, enabling strong decision-making in complex domains like Go and other board games.
• B. MuZero chosen
  MuZero is a DeepMind reinforcement learning algorithm that learns to plan and master complex games like Go, chess, and Atari without being given the rules in advance.
• C. AlphaGo Zero
  AlphaGo Zero is DeepMind's advanced artificial intelligence program that learned to play the board game Go at superhuman level entirely through self-play without human data.
• D. 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.
• E. AlphaZero
  AlphaZero is a DeepMind-developed artificial intelligence system that mastered complex games like chess, shogi, and Go through self-play reinforcement learning without human-crafted strategies.
• F. None of above.
• G. Unsure - the case is ambiguous/there is not enough information to decide.