Triple

T18204459
Position Surface form Disambiguated ID Type / Status
Subject BART E435868 entity
Predicate introducedInPaper P513 FINISHED
Object BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension — NE NERFINISHED

Named-entity recognition

Before disambiguation, gpt-5-mini classified whether the object phrase is a named entity — the step behind the object's NE type shown above.

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: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | Statement: [BART, introducedInPaper, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension]

Disambiguation candidates (1 decision)

The exact options the model was shown at each disambiguation step, with the option it chose highlighted — the evidence behind this triple's disambiguated ids.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
Context triple: [BART, introducedInPaper, BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension]
  • A. Language Models are Unsupervised Multitask Learners
    "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. mBART chosen
    mBART is a multilingual sequence-to-sequence Transformer model designed for tasks like machine translation and text generation across many languages.
  • C. 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.
  • D. 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.
  • E. Neural Programmer-Interpreters
    Neural Programmer-Interpreters are a class of neural network models designed to learn and execute programs by combining differentiable memory, control flow, and modular subroutines for complex algorithmic reasoning tasks.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 batches)

Stage Batch ID Job type Status
creating batch_69d8b90dba6481908e119eb9aa4ca0cb elicitation completed
NER batch_69e4e222831081908f7d5500424e3acb ner completed
Created at: April 10, 2026, 10:32 a.m.