Triple

T8993110
Position Surface form Disambiguated ID Type / Status
Subject Elmo shogi engine E214836 entity
Predicate comparedWith P278 FINISHED
Object AlphaZero (shogi) in DeepMind experiments E40166 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: AlphaZero (shogi) in DeepMind experiments | Statement: [Elmo shogi engine, comparedWith, AlphaZero (shogi) in DeepMind experiments]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AlphaZero (shogi) in DeepMind experiments
Context triple: [Elmo shogi engine, comparedWith, AlphaZero (shogi) in DeepMind experiments]
  • A. 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.
  • B. 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.
  • C. AlphaZero chosen
    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.
  • D. Elmo shogi engine
    Elmo shogi engine is a highly advanced computer program for playing shogi that was strong enough to serve as a benchmark opponent for DeepMind’s AlphaZero.
  • E. 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.
  • 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_69ca83a05c608190bdfdbdb25e994b39 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6876583081909d936dc3c3152587 completed April 1, 2026, 12:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd0cd2b948190947a26fe11ad81bf completed April 3, 2026, 2:38 p.m.
Created at: March 30, 2026, 7:04 p.m.