Triple

T18522845
Position Surface form Disambiguated ID Type / Status
Subject Donald Michie E452632 entity
Predicate notableWork P4 FINISHED
Object MENACE (Matchbox Educable Noughts and Crosses Engine) NE NERFINISHED

How this triple was built (3 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: MENACE (Matchbox Educable Noughts and Crosses Engine) | Statement: [Donald Michie, notableWork, MENACE (Matchbox Educable Noughts and Crosses Engine)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MENACE (Matchbox Educable Noughts and Crosses Engine)
Context triple: [Donald Michie, notableWork, MENACE (Matchbox Educable Noughts and Crosses Engine)]
  • A. 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.
  • B. Leela Chess Zero
    Leela Chess Zero is an open-source, neural-network-based chess engine inspired by AlphaZero that has become one of the strongest and most influential engines in computer chess.
  • C. AlphaStar
    AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
  • D. MuZero
    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.
  • E. the Logic Theorist program
    The Logic Theorist program was an early artificial intelligence system developed in the 1950s that automatically proved theorems in symbolic logic and is often regarded as the first AI program.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MENACE (Matchbox Educable Noughts and Crosses Engine)
Target entity description: MENACE (Matchbox Educable Noughts and Crosses Engine) is an early experimental machine learning system built from matchboxes and beads that learns to play tic-tac-toe through reinforcement.
  • A. 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.
  • B. Leela Chess Zero
    Leela Chess Zero is an open-source, neural-network-based chess engine inspired by AlphaZero that has become one of the strongest and most influential engines in computer chess.
  • C. AlphaStar
    AlphaStar is a DeepMind-created artificial intelligence system that achieved grandmaster-level performance in the real-time strategy game StarCraft II.
  • D. MuZero
    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.
  • E. the Logic Theorist program
    The Logic Theorist program was an early artificial intelligence system developed in the 1950s that automatically proved theorems in symbolic logic and is often regarded as the first AI program.
  • F. None of above. chosen

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_69d8d386df84819092355ebb260d848e completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5338f6da48190bdb374019d10db05 completed April 19, 2026, 7:57 p.m.
Created at: April 10, 2026, 11:37 a.m.