Triple

T1709288
Position Surface form Disambiguated ID Type / Status
Subject Korean Empire E36940 entity
Predicate capital P234 FINISHED
Object Hanseong E317908 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: Hanseong | Statement: [Korean Empire, capital, Hanseong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hanseong
Context triple: [Korean Empire, capital, Hanseong]
  • A. Hanyang
    Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
  • B. Hwaseong
    Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
  • C. Gyeongseong chosen
    Gyeongseong was the Japanese colonial-era name for Seoul, which served as the administrative and political center of Korea under Japanese rule.
  • D. Geumwang-eup
    Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
  • E. Daedeok-gu
    Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
  • 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_69a88617439c819094ffb5d16a0f6307 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa631329088190a2ce8f755bd69fc7 completed March 6, 2026, 5:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69b432e294348190af62caa0d5fce580 completed March 13, 2026, 3:53 p.m.
Created at: March 4, 2026, 7:30 p.m.