Triple

T18204890
Position Surface form Disambiguated ID Type / Status
Subject mBART E435877 entity
Predicate paperTitle P38 FINISHED
Object Multilingual Denoising Pre-training for Neural Machine Translation — NE NERFINISHED

How this triple was built (3 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: Multilingual Denoising Pre-training for Neural Machine Translation | Statement: [mBART, paperTitle, Multilingual Denoising Pre-training for Neural Machine Translation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Multilingual Denoising Pre-training for Neural Machine Translation
Context triple: [mBART, paperTitle, Multilingual Denoising Pre-training for Neural Machine Translation]
  • A. Neural Machine Translation in Linear Time
    "Neural Machine Translation in Linear Time" is a research paper that introduces a more computationally efficient neural architecture for machine translation, reducing translation complexity to linear time with respect to input length.
  • B. Neural Machine Translation by Jointly Learning to Align and Translate
    "Neural Machine Translation by Jointly Learning to Align and Translate" is a seminal research paper that introduced an attention-based neural network architecture for machine translation, enabling models to learn soft alignments between source and target sentences during translation.
  • C. Tensor2Tensor for Neural Machine Translation
    "Tensor2Tensor for Neural Machine Translation" is a research work introducing a modular, scalable library and methodology for training state-of-the-art neural machine translation models.
  • D. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
    "Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
  • E. Google Neural Machine Translation system
    The Google Neural Machine Translation system is Google's deep learning–based framework that provides high-quality, end-to-end neural machine translation across many languages in Google Translate and related services.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Multilingual Denoising Pre-training for Neural Machine Translation
Target entity description: "Multilingual Denoising Pre-training for Neural Machine Translation" is the research paper that introduces mBART, a sequence-to-sequence denoising autoencoder pre-trained on large-scale multilingual data to improve neural machine translation across many languages.
  • A. Neural Machine Translation in Linear Time
    "Neural Machine Translation in Linear Time" is a research paper that introduces a more computationally efficient neural architecture for machine translation, reducing translation complexity to linear time with respect to input length.
  • B. Neural Machine Translation by Jointly Learning to Align and Translate
    "Neural Machine Translation by Jointly Learning to Align and Translate" is a seminal research paper that introduced an attention-based neural network architecture for machine translation, enabling models to learn soft alignments between source and target sentences during translation.
  • C. Tensor2Tensor for Neural Machine Translation
    "Tensor2Tensor for Neural Machine Translation" is a research work introducing a modular, scalable library and methodology for training state-of-the-art neural machine translation models.
  • D. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
    "Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.
  • E. Google Neural Machine Translation system
    The Google Neural Machine Translation system is Google's deep learning–based framework that provides high-quality, end-to-end neural machine translation across many languages in Google Translate and related services.
  • F. None of above. chosen

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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
Created at: April 10, 2026, 10:32 a.m.