Triple

T17546063
Position Surface form Disambiguated ID Type / Status
Subject Sonic Nurse E427326 entity
Predicate hasPart P35 FINISHED
Object Pattern Recognition 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: Pattern Recognition | Statement: [Sonic Nurse, hasPart, Pattern Recognition]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pattern Recognition
Context triple: [Sonic Nurse, hasPart, Pattern Recognition]
  • A. IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence
    The IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence is a leading professional body that advances research and standards in computer vision, pattern recognition, and machine learning within the IEEE community.
  • B. The Psychology of Computer Vision (edited volume)
    The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
  • C. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • D. Cover’s theorem on the separability of patterns
    Cover’s theorem on the separability of patterns is a fundamental result in statistical learning theory stating that complex pattern-classification problems are more likely to be linearly separable when data are mapped into a higher-dimensional feature space.
  • E. IEEE Transactions on Pattern Analysis and Machine Intelligence
    IEEE Transactions on Pattern Analysis and Machine Intelligence is a leading peer-reviewed journal that publishes cutting-edge research in computer vision, pattern recognition, and machine learning.
  • 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: Pattern Recognition
Target entity description: "Pattern Recognition" is a track by the American alternative rock band Sonic Youth, featured on their 2004 album *Sonic Nurse*.
  • A. IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence
    The IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence is a leading professional body that advances research and standards in computer vision, pattern recognition, and machine learning within the IEEE community.
  • B. The Psychology of Computer Vision (edited volume)
    The Psychology of Computer Vision is an influential edited volume, compiled by Patrick Henry Winston, that brings together foundational research exploring how principles of human perception and cognition can inform and advance computer vision.
  • C. Gradient-based learning applied to document recognition
    "Gradient-based learning applied to document recognition" is a seminal 1998 paper by Yann LeCun and colleagues that introduced and demonstrated the effectiveness of convolutional neural networks for tasks like handwritten digit recognition, helping to lay the foundations of modern deep learning.
  • D. Cover’s theorem on the separability of patterns
    Cover’s theorem on the separability of patterns is a fundamental result in statistical learning theory stating that complex pattern-classification problems are more likely to be linearly separable when data are mapped into a higher-dimensional feature space.
  • E. IEEE Transactions on Pattern Analysis and Machine Intelligence
    IEEE Transactions on Pattern Analysis and Machine Intelligence is a leading peer-reviewed journal that publishes cutting-edge research in computer vision, pattern recognition, and machine learning.
  • 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_69d889df6dc081908f67dbadc03c07ee completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e45461643881909b106bafb89253b3 completed April 19, 2026, 4:04 a.m.
Created at: April 10, 2026, 5:49 a.m.