Triple

T5910835
Position Surface form Disambiguated ID Type / Status
Subject Alex Krizhevsky E131452 entity
Predicate usedDataset P11520 FINISHED
Object ImageNet E363692 NE FINISHED

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: ImageNet | Statement: [Alex Krizhevsky, usedDataset, ImageNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ImageNet
Context triple: [Alex Krizhevsky, usedDataset, ImageNet]
  • A. ImageNet chosen
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • B. CIFAR
    CIFAR (the Canadian Institute for Advanced Research) is a Canadian global research organization that supports long-term, collaborative, interdisciplinary research, including major initiatives in artificial intelligence.
  • 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. AlexNet
    AlexNet is a pioneering deep convolutional neural network architecture that dramatically advanced image recognition performance and helped spark the modern deep learning revolution after winning the 2012 ImageNet competition.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: usedDataset
Context triple: [Alex Krizhevsky, usedDataset, ImageNet]
  • A. usedDataFrom chosen
    Indicates that one entity utilized or relied on data originating from another entity.
  • B. evaluationDataset
    Indicates that a dataset is used as a benchmark or test set for evaluating the performance or quality of a system, model, or method.
  • C. usedAt
    Indicates that something is employed, applied, or utilized at a particular place, time, or context.
  • D. usedCross
    Indicates that one entity made use of a cross-shaped object or structure, or traversed by means of a crossing point such as a crosswalk or intersection.
  • E. usedDocument
    Indicates that one entity has employed, referenced, or otherwise made use of a particular document in performing an action or fulfilling a purpose.
  • F. None of above.

Provenance (4 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_69c008593a44819081a07ae0efe6c574 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c048fc112c8190b905bf561c9de096 completed March 22, 2026, 7:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0b17375488190a3053d37712501b3 completed March 23, 2026, 3:20 a.m.
PD Predicate disambiguation batch_69c03352208c8190968efed05a9fd416 completed March 22, 2026, 6:22 p.m.
Created at: March 22, 2026, 3:59 p.m.