Triple

T29106601
Position Surface form Disambiguated ID Type / Status
Subject Sinan County, South Jeolla, South Korea E736777 entity
Predicate hasPart P35 FINISHED
Object Anjwa-myeon
Anjwa-myeon is a rural township-level administrative division located within Sinan County in South Jeolla Province, South Korea, known for its coastal and island landscapes.
E1967516 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: Anjwa-myeon | Statement: [Sinan County, South Jeolla, South Korea, hasPart, Anjwa-myeon]
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: Anjwa-myeon
Triple: [Sinan County, South Jeolla, South Korea, hasPart, Anjwa-myeon]
Generated description
Anjwa-myeon is a rural township-level administrative division located within Sinan County in South Jeolla Province, South Korea, known for its coastal and island landscapes.

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_69f077ec765c81909474c88bcc8bab43 completed April 28, 2026, 9:03 a.m.
NER Named-entity recognition batch_69f661ba062881909fa3d7b23938e2ab completed May 2, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2b2d58cca0819089ff34a68224379f completed June 11, 2026, 9:49 p.m.
NEDg Description generation batch_6a2b2f981fa08190b5da54a3acc55edb completed June 11, 2026, 9:58 p.m.
NED2 Entity disambiguation (via description) batch_6a2b3010e6408190af561a3bacdef55b completed June 11, 2026, 10 p.m.
Created at: April 28, 2026, 11:16 a.m.