Triple

T4651216
Position Surface form Disambiguated ID Type / Status
Subject Language Models are Few-Shot Learners E102297 entity
Predicate author P4 FINISHED
Object Dario Amodei E99318 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: Dario Amodei | Statement: [Language Models are Few-Shot Learners, author, Dario Amodei]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dario Amodei
Context triple: [Language Models are Few-Shot Learners, author, Dario Amodei]
  • A. Dario Amodei chosen
    Dario Amodei is an AI researcher and entrepreneur, co-founder and CEO of Anthropic and former OpenAI research leader known for his work on large language models and AI safety.
  • B. 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.
  • C. Jonathon Shlens
    Jonathon Shlens is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work at Google.
  • D. 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.
  • E. Wolfram Burgard
    Wolfram Burgard is a German computer scientist and roboticist known for his influential work in probabilistic robotics, autonomous navigation, and artificial intelligence.
  • 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_69bd43d71a308190afea7280841b0de8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd630343f88190954d19fcd18a5864 completed March 20, 2026, 3:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69be39b295ec8190bab91913ddf8eb8d completed March 21, 2026, 6:24 a.m.
Created at: March 20, 2026, 1:14 p.m.