Triple

T27358020
Position Surface form Disambiguated ID Type / Status
Subject Seocheon County E685739 entity
Predicate hasAttraction P105 FINISHED
Object Hansan Mosi Village
Hansan Mosi Village is a traditional Korean village in Seocheon renowned for its centuries-old craftsmanship of fine ramie (mosi) weaving and its well-preserved cultural heritage.
E1769131 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: Hansan Mosi Village | Statement: [Seocheon County, hasAttraction, Hansan Mosi Village]
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: Hansan Mosi Village
Triple: [Seocheon County, hasAttraction, Hansan Mosi Village]
Generated description
Hansan Mosi Village is a traditional Korean village in Seocheon renowned for its centuries-old craftsmanship of fine ramie (mosi) weaving and its well-preserved cultural heritage.

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_69ef14887c288190931b8431fdbf53c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62c20321481908ba574ad96d93013 completed May 2, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7d88f8481909b162a1266aaf4e5 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a8b8ec608190846c55dabeec801a completed May 24, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_6a12a951f04881909419d5b9d41d5c79 completed May 24, 2026, 7:31 a.m.
Created at: April 27, 2026, 11:52 a.m.