Triple

T4586028
Position Surface form Disambiguated ID Type / Status
Subject Double DQN E101969 entity
Predicate introducedBy P513 FINISHED
Object David Silver E41249 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: David Silver | Statement: [Double DQN, introducedBy, David Silver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Silver
Context triple: [Double DQN, introducedBy, David Silver]
  • A. David Silver chosen
    David Silver is a leading artificial intelligence researcher best known for his work at DeepMind on reinforcement learning and the development of the AlphaGo system.
  • B. David Silver
    David Silver is a central character on the teen drama series "Beverly Hills, 90210," known for his evolution from an awkward outsider to a popular DJ and radio host.
  • C. Demis Hassabis
    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.
  • D. 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.
  • E. Sergey Levine
    Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
  • 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_69bd43d4ce208190b53158c882b222e3 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5906a43c81908fb11bf8f94be122 completed March 20, 2026, 2:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde0aa114881909fe446bf86c675e7 completed March 21, 2026, 12:04 a.m.
Created at: March 20, 2026, 1:10 p.m.