Triple

T1793228
Position Surface form Disambiguated ID Type / Status
Subject WaveNet E39544 entity
Predicate introducedBy P513 FINISHED
Object Oriol Vinyals E46146 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: Oriol Vinyals | Statement: [WaveNet, introducedBy, Oriol Vinyals]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Oriol Vinyals
Context triple: [WaveNet, introducedBy, Oriol Vinyals]
  • A. Oriol Vinyals chosen
    Oriol Vinyals is a prominent computer scientist and machine learning researcher known for his influential work on deep learning, sequence-to-sequence models, and reinforcement learning at leading AI labs.
  • B. 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.
  • C. Sergey Levine
    Sergey Levine is a prominent computer scientist and professor known for his influential research in deep reinforcement learning and robotics.
  • D. 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.
  • E. Pieter Abbeel
    Pieter Abbeel is a Belgian-American computer scientist and professor at UC Berkeley known for his influential work in robotics and deep reinforcement learning.
  • 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_69a88631854081909723959921e45c2b completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa653b02448190bc475bc22187f5b0 completed March 6, 2026, 5:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69adb5d26afc81909675064289d3a5b8 completed March 8, 2026, 5:45 p.m.
Created at: March 4, 2026, 7:32 p.m.