SOV20190429B.lst

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You are a storyteller. Create a narrative in the Korean language within 300 words based on a provided dataset that uses an ontology-based structure.
The dataset is divided into five main sections, each marked with a hashtag (#) followed by the section title. These sections include:

#Topic: Describes the main subject or theme of the dataset.
#Class: Contains a list of classes that categorize the types of objects or entities present in the data.
#Relation: Enumerates the types of relationships or connections between the objects or entities.
#Nodes: Lists individual objects or entities identified in the dataset, along with their attributes such as ID, class, label, web page URL, icon image URL, and remark (optional)
#Links: Provides a list of RDF triples that describe the relationships between objects or entities, specified by source, target, relation, and remark (optional)
#End: Signifies the end of the semantic data.

Using the information provided in the dataset, craft a narrative that encapsulates the key themes, relationships, and entities. The narrative should accurately reflect the semantic structure and content of the dataset, making it accessible and engaging for readers.

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#Topic
h1 Tutorial for NZ 20194029 Class

#Class
Person
Place
Coffee

#Relation
livesIn
likes
isBrotherOf
isSisterOf

#Nodes
Bart, Person, Bart, https://en.wikipedia.org/wiki/Bart_Simpson, http://dh.aks.ac.kr/~tutor/Images/NZ/Bart.png
Lisa, Person, Lisa, https://en.wikipedia.org/wiki/Lisa_Simpson, http://dh.aks.ac.kr/~tutor/Images/NZ/Lisa.png
ROK, Place, South Korea, https://en.wikipedia.org/wiki/South_Korea, No URL
NZ, Place, New Zealand, https://en.wikipedia.org/wiki/New_Zealand, No URL
LongBlack, Coffee, Long Black, https://en.wikipedia.org/wiki/Long_black, No URL
FlatWhite, Coffee, Flat White, https://en.wikipedia.org/wiki/Flat_white, No URL

#Links
Bart, ROK, livesIn
Lisa, NZ, livesIn
Bart, FlatWhite, likes
Lisa, LongBlack, likes
Bart, Lisa, isBrotherOf
Lisa, Bart, isSisterOf

#End