Triple

T19190096
Position Surface form Disambiguated ID Type / Status
Subject GPT-1 E469810 entity
Predicate coAuthor P398 FINISHED
Object Tim Salimans 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: Tim Salimans | Statement: [GPT-1, coAuthor, Tim Salimans]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tim Salimans
Context triple: [GPT-1, coAuthor, Tim Salimans]
  • A. Tim Salimans chosen
    Tim Salimans is a machine learning researcher known for influential work in generative models and evaluation metrics, including the development of the Inception Score for assessing image generation quality.
  • B. 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.
  • C. 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.
  • D. 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).
  • E. Jakob Uszkoreit
    Jakob Uszkoreit is a computer scientist and AI researcher best known as one of the co-authors of the seminal "Attention Is All You Need" paper that introduced the Transformer architecture.
  • 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.