Triple

T5910818
Position Surface form Disambiguated ID Type / Status
Subject Alex Krizhevsky E131452 entity
Predicate coDeveloperOf P6901 FINISHED
Object AlexNet E74105 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: AlexNet | Statement: [Alex Krizhevsky, coDeveloperOf, AlexNet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AlexNet
Context triple: [Alex Krizhevsky, coDeveloperOf, AlexNet]
  • A. AlexNet chosen
    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.
  • B. LeNet
    LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
  • C. GoogLeNet
    GoogLeNet is a deep convolutional neural network developed by Google that popularized the Inception architecture and achieved state-of-the-art performance in image recognition tasks.
  • D. ResNet
    ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
  • E. VGG
    VGG is a deep convolutional neural network architecture known for its simple, uniform use of small 3×3 filters and great depth, which achieved strong performance in image recognition tasks.
  • 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_69c008593a44819081a07ae0efe6c574 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c037767af08190b5678cff9fd0816f completed March 22, 2026, 6:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0c015b90081909777ac5ed80e927c completed March 23, 2026, 4:22 a.m.
Created at: March 22, 2026, 3:59 p.m.