Triple

T11003587
Position Surface form Disambiguated ID Type / Status
Subject Matching Networks for One Shot Learning E260058 entity
Predicate title P38 FINISHED
Object Matching Networks for One Shot Learning E260058 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: Matching Networks for One Shot Learning | Statement: [Matching Networks for One Shot Learning, title, Matching Networks for One Shot Learning]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Matching Networks for One Shot Learning
Context triple: [Matching Networks for One Shot Learning, title, Matching Networks for One Shot Learning]
  • A. Matching Networks for One Shot Learning chosen
    "Matching Networks for One Shot Learning" is a seminal deep learning paper that introduced a metric-based approach for one-shot image classification using attention and memory-augmented neural networks.
  • B. Learning Transferable Architectures for Scalable Image Recognition
    "Learning Transferable Architectures for Scalable Image Recognition" is a research paper that introduced NASNet, a neural architecture search–designed convolutional network that achieved state-of-the-art performance on large-scale image recognition tasks.
  • C. Long-term Recurrent Convolutional Networks for Visual Recognition and Description
    "Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
  • D. “Learning representations by back-propagating errors”
    “Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
  • E. Hebbian learning
    Hebbian learning is a neurobiological and computational learning principle often summarized as "cells that fire together wire together," where the connection between neurons is strengthened when they are activated simultaneously.
  • 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.