Triple

T15361459
Position Surface form Disambiguated ID Type / Status
Subject CIFAR-10 E367298 entity
Predicate partOf P40 FINISHED
Object CIFAR datasets E35343 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: CIFAR datasets | Statement: [CIFAR-10, partOf, CIFAR datasets]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CIFAR datasets
Context triple: [CIFAR-10, partOf, CIFAR datasets]
  • A. CIFAR chosen
    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.
  • B. 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.
  • C. 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.
  • D. TensorFlow Datasets
    TensorFlow Datasets is a collection of ready-to-use, standardized datasets for machine learning and deep learning workflows in TensorFlow and other frameworks.
  • 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.
  • 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_69d85a1483788190ad93c2748e8af34b completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e4607408190ab281a7f7a8012d3 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1343862481908962dfe0ab946b97 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:18 a.m.