Triple

T2373759
Position Surface form Disambiguated ID Type / Status
Subject Oriol Vinyals E46146 entity
Predicate notableWork P4 FINISHED
Object Sequence to Sequence Learning with Neural Networks E260048 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: Sequence to Sequence Learning with Neural Networks | Statement: [Oriol Vinyals, notableWork, Sequence to Sequence Learning with Neural Networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sequence to Sequence Learning with Neural Networks
Context triple: [Oriol Vinyals, notableWork, Sequence to Sequence Learning with Neural Networks]
  • A. Sequence to Sequence Learning with Neural Networks chosen
    "Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.
  • B. 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.
  • C. Neural Turing Machines (contributions)
    Neural Turing Machines (contributions) refers to Oriol Vinyals’s work on augmenting neural networks with differentiable external memory to enable algorithmic reasoning and sequence learning beyond traditional architectures.
  • D. Pointer Networks
    Pointer Networks are a type of neural network architecture that uses attention mechanisms to output discrete positions in an input sequence, enabling solutions to combinatorial problems like sorting and the traveling salesman problem.
  • E. Distributed Representations of Sentences and Documents
    "Distributed Representations of Sentences and Documents" is a seminal machine learning paper that introduced the Paragraph Vector (Doc2Vec) method for learning continuous vector representations of variable-length text such as sentences, paragraphs, and documents.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc791c4688190a4b8f0e540e84eb4 completed March 7, 2026, 6:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69aeb3ca7ef88190902e1c8429694a82 completed March 9, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:56 p.m.