Triple

T15555783
Position Surface form Disambiguated ID Type / Status
Subject Jian Sun E370862 entity
Predicate coAuthorWith P398 FINISHED
Object Kaiming He E367295 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: Kaiming He | Statement: [Jian Sun, coAuthorWith, Kaiming He]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaiming He
Context triple: [Jian Sun, coAuthorWith, Kaiming He]
  • A. Kaiming He chosen
    Kaiming He is a prominent Chinese computer scientist known for pioneering deep learning architectures and techniques, including the influential ResNet model for image recognition.
  • B. Jia Deng
    Jia Deng is a computer scientist known for his influential work in computer vision and machine learning, particularly as a co-creator of the large-scale image dataset ImageNet.
  • C. Jiawei Han
    Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
  • D. Abraham Girshick
    Abraham Girshick was an American statistician known for his contributions to statistical decision theory and his work during World War II with Columbia University's Statistical Research Group.
  • E. Christian Szegedy
    Christian Szegedy is a computer scientist and AI researcher known for his influential work on deep learning and convolutional neural networks, including contributions to the Inception architecture.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a97dbfc8190a98cbbac5e71ba88 completed April 16, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff456427988190bddea01f5cb159d9 completed May 9, 2026, 2:32 p.m.
Created at: April 10, 2026, 4:09 a.m.