Triple

T27265164
Position Surface form Disambiguated ID Type / Status
Subject Chungcheongnam-do E687880 entity
Predicate locatedIn P40 FINISHED
Object western South Korea
Western South Korea is the region along the country’s Yellow Sea coast, encompassing provinces such as Chungcheongnam-do and characterized by coastal plains, industrial cities, and major ports.
 E1644902 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: western South Korea | Statement: [Chungcheongnam-do, locatedIn, western South Korea]
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: western South Korea
Triple: [Chungcheongnam-do, locatedIn, western South Korea]
Generated description
Western South Korea is the region along the country’s Yellow Sea coast, encompassing provinces such as Chungcheongnam-do and characterized by coastal plains, industrial cities, and major ports.

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_69ef3557abc481908bf3c146f0f3356a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f626f2c8008190b1e24ed5d521eaa3 completed May 2, 2026, 4:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12628966388190b8a992b91f3183d4 completed May 24, 2026, 2:29 a.m.
NEDg Description generation batch_6a1263c16b7c8190bf1e6d9a48f04e79 completed May 24, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_6a12645e001081909536516633a422a9 completed May 24, 2026, 2:37 a.m.
Created at: April 27, 2026, 10:55 a.m.