Triple

T22411993
Position Surface form Disambiguated ID Type / Status
Subject ImageNet Classification with Deep Convolutional Neural Networks E554013 entity
Predicate benchmark P17142 FINISHED
Object ILSVRC-2010 — 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: ILSVRC-2010 | Statement: [ImageNet Classification with Deep Convolutional Neural Networks, benchmark, ILSVRC-2010]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: ILSVRC-2010
Context triple: [ImageNet Classification with Deep Convolutional Neural Networks, benchmark, ILSVRC-2010]
  • A. ImageNet chosen
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • B. SVRC
    SVRC is the abbreviation for the Singapore Volunteer Rifle Corps, a 19th-century volunteer military unit formed to bolster the defense of colonial Singapore.
  • C. CIFAR-10
    CIFAR-10 is a widely used computer vision dataset of 60,000 labeled low-resolution images across 10 object classes, commonly employed to benchmark image classification algorithms.
  • D. CIFAR-100
    CIFAR-100 is a widely used image classification dataset consisting of 60,000 32×32 color images across 100 object categories, commonly used to benchmark machine learning models.
  • E. ImageNet CNN
    ImageNet CNN is a convolutional neural network model trained on the large-scale ImageNet dataset, commonly used as a powerful pretrained feature extractor for various computer vision tasks.
  • 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_69e11e4e6ce8819085a1e06d886bf21c completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15943dd84819099e77563da470594 completed April 29, 2026, 1:05 a.m.
Created at: April 16, 2026, 8:46 p.m.