Triple

T1923049
Position Surface form Disambiguated ID Type / Status
Subject AlphaZero E40166 entity
Predicate publicationTitle P33185 FINISHED
Object A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play E40166 NE FINISHED

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: A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play | Statement: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play
Context triple: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
  • A. 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.
  • 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. 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.
  • D. 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.
  • E. AlphaGo
    AlphaGo is an artificial intelligence program developed by DeepMind that became famous for defeating world champion Go players using deep neural networks and reinforcement learning.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: publicationTitle
Context triple: [AlphaZero, publicationTitle, A general reinforcement learning algorithm that masters chess, shogi, and Go through self‑play]
  • A. publicationType
    Indicates the specific category or format of a published work that characterizes how it is issued or presented.
  • B. publishedIn
    Indicates that a work (such as an article, paper, or book) has been formally released or made available within a specific venue, medium, or publication.
  • C. publishedAs
    Indicates that an entity is released, issued, or made publicly available under a particular name, format, or identity.
  • D. publicationCompleted
    Indicates that the process of publishing an item (such as a document, work, or release) has been fully finished and made officially available.
  • E. formerPublication
    Indicates that an entity was previously published in, or associated as a publication with, another entity but is no longer currently so.
  • F. None of above. chosen

Provenance (5 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_69a8864298748190a2f2fd34f7ef8d77 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb23459ac819088ded5bfac9d4aad completed March 7, 2026, 5:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3e6678881908d72de7e0f19a648 completed March 8, 2026, 10:10 p.m.
PD Predicate disambiguation batch_69abafed2ab481908920334e77b1021b completed March 7, 2026, 4:56 a.m.
PDg Predicate description generation batch_69abb1b180e481908bbe893d6ba6208b completed March 7, 2026, 5:03 a.m.
Created at: March 4, 2026, 7:35 p.m.