Triple

T15218194
Position Surface form Disambiguated ID Type / Status
Subject ImageNet E363692 entity
Predicate abbreviation P43 FINISHED
Object ILSVRC E363692 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: ILSVRC | Statement: [ImageNet, abbreviation, ILSVRC]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ILSVRC
Context triple: [ImageNet, abbreviation, ILSVRC]
  • A. ImageNet chosen
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • B. 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.
  • C. 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.
  • D. LSRC
    LSRC is a major interdisciplinary science and engineering research facility at Duke University that houses laboratories, classrooms, and collaborative spaces for multiple scientific disciplines.
  • E. ImageNet Classification with Deep Convolutional Neural Networks
    "ImageNet Classification with Deep Convolutional Neural Networks" is the landmark 2012 research paper that introduced the deep CNN model AlexNet, demonstrating a dramatic leap in image recognition performance on the ImageNet benchmark and catalyzing the modern deep learning revolution in computer vision.
  • 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_69d85a0ce24c81909c4d3b6475548c95 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0076f90c481909989befe031a2cae completed April 15, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd3159fc81908c05cfbd0bd7e5ac completed May 9, 2026, 7:07 a.m.
Created at: April 10, 2026, 3:11 a.m.