Triple

T4707025
Position Surface form Disambiguated ID Type / Status
Subject Jeff Wu E104412 entity
Predicate coAuthorOf P2389 FINISHED
Object Language Models are Unsupervised Multitask Learners E437278 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: Language Models are Unsupervised Multitask Learners | Statement: [Jeff Wu, coAuthorOf, Language Models are Unsupervised Multitask Learners]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Language Models are Unsupervised Multitask Learners
Context triple: [Jeff Wu, coAuthorOf, Language Models are Unsupervised Multitask Learners]
  • A. Language Models are Unsupervised Multitask Learners chosen
    "Language Models are Unsupervised Multitask Learners" is a 2019 OpenAI research paper that demonstrated how large-scale unsupervised language models like GPT-2 can perform a wide range of tasks without task-specific training.
  • B. Exploring the Limits of Language Modeling
    "Exploring the Limits of Language Modeling" is a research paper that investigates how far large-scale neural language models can be pushed in terms of performance, scalability, and generalization on natural language tasks.
  • C. Language Models are Few-Shot Learners
    "Language Models are Few-Shot Learners" is a landmark research paper that demonstrated large-scale transformer-based language models can perform diverse tasks from just a few examples without task-specific training.
  • D. Attention Is All You Need
    "Attention Is All You Need" is the landmark 2017 research paper that introduced the Transformer architecture and revolutionized modern natural language processing and sequence modeling.
  • E. LLaMA
    LLaMA is a family of large language models developed by Meta AI, designed for efficient training and inference across a range of natural language processing tasks.
  • 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_69bd43eac3c08190af7e4020c6c3704c completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd63e9f0b88190820aa7fba2f91b6e completed March 20, 2026, 3:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69be106d1fc08190808c19025c6f37c4 completed March 21, 2026, 3:28 a.m.
Created at: March 20, 2026, 1:17 p.m.