Triple

T9500708
Position Surface form Disambiguated ID Type / Status
Subject AlphaGo Zero E229130 entity
Predicate hasAuthor P4244 FINISHED
Object Demis Hassabis E39539 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: Demis Hassabis | Statement: [AlphaGo Zero, hasAuthor, Demis Hassabis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Demis Hassabis
Context triple: [AlphaGo Zero, hasAuthor, Demis Hassabis]
  • A. Demis Hassabis chosen
    Demis Hassabis is a British artificial intelligence researcher, neuroscientist, and entrepreneur best known as the co-founder and CEO of DeepMind, a leading AI company acquired by Google.
  • B. Julian Schrittwieser
    Julian Schrittwieser is a computer scientist and AI researcher known for his work at DeepMind on advanced reinforcement learning and game-playing systems such as AlphaZero.
  • C. Shane Legg
    Shane Legg is a computer scientist and AI researcher best known as a co-founder of DeepMind and for his influential work on artificial general intelligence.
  • D. Ilya Sutskever
    Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
  • E. Nicolas Heess
    Nicolas Heess is a machine learning researcher known for his work in deep reinforcement learning, including contributions to algorithms such as Deep Deterministic Policy Gradient (DDPG).
  • 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_69ca84753660819098e8d416e89e26ae completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd983c308c8190bde6858ac1ca8ea5 completed April 1, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69d13a0a5ec881908bb1643d2bea2c9f completed April 4, 2026, 4:19 p.m.
Created at: March 30, 2026, 7:57 p.m.