Triple

T9412970
Position Surface form Disambiguated ID Type / Status
Subject Paju E226748 entity
Predicate locatedIn P40 FINISHED
Object Sudogwon E227776 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: Sudogwon | Statement: [Paju, locatedIn, Sudogwon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sudogwon
Context triple: [Paju, locatedIn, Sudogwon]
  • A. Sudogwon chosen
    Sudogwon is the Seoul Capital Area of South Korea, encompassing Seoul, Incheon, and surrounding Gyeonggi Province as the country’s largest and most populous metropolitan region.
  • B. Gwangalli
    Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
  • C. Seochon
    Seochon is a historic neighborhood in central Seoul known for its traditional hanok houses, narrow alleyways, and vibrant mix of old Korean culture and modern cafes and galleries.
  • D. Wiryeseong
    Wiryeseong was the first capital city of the ancient Korean kingdom of Baekje, located in the Han River basin near present-day Seoul.
  • E. Won-dong
    Won-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
  • 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_69ca843280488190bc65600e843ef9e6 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd5258f7e081908d48600409181fdb completed April 1, 2026, 5:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d107b0f9648190894a4cd13d7e5fb5 completed April 4, 2026, 12:44 p.m.
Created at: March 30, 2026, 7:47 p.m.