Triple

T19190097
Position Surface form Disambiguated ID Type / Status
Subject GPT-1 E469810 entity
Predicate coAuthor P398 FINISHED
Object Ilya Sutskever NE NERFINISHED

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: Ilya Sutskever | Statement: [GPT-1, coAuthor, Ilya Sutskever]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ilya Sutskever
Context triple: [GPT-1, coAuthor, Ilya Sutskever]
  • A. Ilya Sutskever chosen
    Ilya Sutskever is a leading artificial intelligence researcher and co-founder of OpenAI, known for his pioneering work in deep learning and neural networks.
  • B. Ilya Goodfellow
    Ilya Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and contributing to deep learning at organizations like Google and OpenAI.
  • C. Volodymyr Mnih
    Volodymyr Mnih is a computer scientist and deep learning researcher known for pioneering deep reinforcement learning methods that achieved human-level performance on Atari games.
  • D. Alex Krizhevsky
    Alex Krizhevsky is a computer scientist best known for co-developing the AlexNet convolutional neural network, which revolutionized deep learning in computer vision.
  • E. Ian Goodfellow
    Ian Goodfellow is a machine learning researcher best known for inventing Generative Adversarial Networks (GANs) and co-authoring the influential textbook "Deep Learning."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd0ad9088190a173b32657ae2e7a completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5f8a16e20819080baa5112f000b41 completed April 20, 2026, 9:57 a.m.
Created at: April 10, 2026, 12:07 p.m.