Triple

T11003395
Position Surface form Disambiguated ID Type / Status
Subject Łukasz Kaiser E260054 entity
Predicate coAuthorOf P2389 FINISHED
Object Attention Is All You Need E457850 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: Attention Is All You Need | Statement: [Łukasz Kaiser, 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: [Łukasz Kaiser, 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. 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.
  • C. 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.
  • D. Sequence to Sequence Learning with Neural Networks
    "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.
  • E. 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.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797546f448190946ee6442d657dc5 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3453d181081908cb58a957f4d1295 completed April 18, 2026, 8:47 a.m.
Created at: April 8, 2026, 9:25 p.m.