Triple

T18724010
Position Surface form Disambiguated ID Type / Status
Subject Attention Is All You Need E457850 entity
Predicate replacedArchitecture P10258 FINISHED
Object LSTM networks — 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: LSTM networks | Statement: [Attention Is All You Need, replacedArchitecture, LSTM networks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LSTM networks
Context triple: [Attention Is All You Need, replacedArchitecture, LSTM networks]
  • A. LSTM networks chosen
    LSTM networks are a type of recurrent neural network architecture designed to effectively capture long-term dependencies in sequential data by using gated memory cells.
  • B. recurrent neural networks
    Recurrent neural networks are a class of artificial neural networks designed to process sequential data by maintaining and updating a hidden state that captures information over time.
  • C. Generating sequences with recurrent neural networks
    "Generating Sequences with Recurrent Neural Networks" is a highly influential research paper by Alex Graves that advanced the use of RNNs for tasks like handwriting and text generation by demonstrating powerful sequence modeling and generation capabilities.
  • D. Sequence transduction with recurrent neural networks
    "Sequence transduction with recurrent neural networks" is a seminal research paper by Alex Graves that introduced powerful RNN-based methods for mapping input sequences to output sequences, influencing modern sequence-to-sequence and attention models in machine learning.
  • E. 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.
  • 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_69d8d393ba9c8190a8b03b04ddbb0a09 completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e56abcfc048190a01dee959e768768 completed April 19, 2026, 11:52 p.m.
Created at: April 10, 2026, 11:50 a.m.