Triple

T19843646
Position Surface form Disambiguated ID Type / Status
Subject Illia Polosukhin E476800 entity
Predicate coAuthorOf P2389 FINISHED
Object Attention Is All You Need — NE NERFINISHED

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: Attention Is All You Need | Statement: [Illia Polosukhin, coAuthorOf, Attention Is All You Need]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Attention Is All You Need
Context triple: [Illia Polosukhin, coAuthorOf, Attention Is All You Need]
  • A. Attention Is All You Need chosen
    "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.
  • B. Bidirectional Encoder Representations from Transformers
    Bidirectional Encoder Representations from Transformers (BERT) is a widely used deep learning language model developed by Google that learns contextual word representations by jointly conditioning on both left and right context in text.
  • C. Improving Language Understanding by Generative Pre-Training
    "Improving Language Understanding by Generative Pre-Training" is the original research paper that introduced the GPT-1 model and demonstrated the effectiveness of large-scale unsupervised pretraining for natural language processing tasks.
  • D. Transformer-XL
    Transformer-XL is a neural network architecture for language modeling that extends the Transformer with segment-level recurrence and relative positional encodings to better capture long-range dependencies.
  • E. Big Bird: Transformers for Longer Sequences
    "Big Bird: Transformers for Longer Sequences" is a research paper that introduces a sparse-attention Transformer architecture enabling efficient processing of much longer input sequences than standard Transformers while retaining strong performance.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d8e51d39d081909bcfafeaaf3d2fcc completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65806ea888190850421154238d91c completed April 20, 2026, 4:44 p.m.
Created at: April 10, 2026, 1:51 p.m.