Triple

T8482979
Position Surface form Disambiguated ID Type / Status
Subject Rainbow DQN E200562 entity
Predicate proposedBy P32 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: [Rainbow DQN, proposedBy, David Silver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Silver
Context triple: [Rainbow DQN, proposedBy, 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe53845e881909eeb32863c7aa942 completed March 31, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebb5dbfc08190827e8e886a37a8be completed April 2, 2026, 6:54 p.m.
Created at: March 30, 2026, 6:12 p.m.