Triple

T3542984
Position Surface form Disambiguated ID Type / Status
Subject ResNet E74928 entity
Predicate benchmarkedOn P23745 FINISHED
Object 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.
 E367298 NE FINISHED

How this triple was built (4 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: CIFAR-10 | Statement: [ResNet, benchmarkedOn, CIFAR-10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CIFAR-10
Context triple: [ResNet, benchmarkedOn, CIFAR-10]
  • A. MNIST
    MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
  • 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. Fashion-MNIST
    Fashion-MNIST is a popular benchmark dataset of Zalando clothing item images used as a more challenging drop-in replacement for the original MNIST handwritten digits in machine learning research.
  • D. KMNIST
    KMNIST is a benchmark image dataset of handwritten Japanese characters (hiragana) designed as a more complex, drop-in replacement for the original MNIST digit dataset.
  • E. ImageNet
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: CIFAR-10
Triple: [ResNet, benchmarkedOn, CIFAR-10]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CIFAR-10
Target entity description: 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.
  • A. MNIST
    MNIST is a widely used benchmark dataset of handwritten digit images commonly employed for training and evaluating image classification algorithms in machine learning and computer vision.
  • 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. Fashion-MNIST
    Fashion-MNIST is a popular benchmark dataset of Zalando clothing item images used as a more challenging drop-in replacement for the original MNIST handwritten digits in machine learning research.
  • D. KMNIST
    KMNIST is a benchmark image dataset of handwritten Japanese characters (hiragana) designed as a more complex, drop-in replacement for the original MNIST digit dataset.
  • E. ImageNet
    ImageNet is a large-scale visual database widely used for training and benchmarking image classification and computer vision algorithms.
  • F. None of above. chosen

Provenance (5 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_69ad85d274cc8190ab59c97298a1cfbf completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbf752dd481909226044ffe595338 completed March 8, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bdd0cb4819086119b54c2708850 completed March 13, 2026, 4 a.m.
NEDg Description generation batch_69b38cb6a2188190b68f4903144a0e51 completed March 13, 2026, 4:04 a.m.
NED2 Entity disambiguation (via description) batch_69b39062a10c8190bc227c02cf4f3ab1 completed March 13, 2026, 4:19 a.m.
Created at: March 8, 2026, 3:20 p.m.