Triple

T14427497
Position Surface form Disambiguated ID Type / Status
Subject Mongol invasion of the Korean Peninsula E357732 entity
Predicate notableLocation P3858 FINISHED
Object Kaesong E503225 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: Kaesong | Statement: [Mongol invasion of the Korean Peninsula, notableLocation, Kaesong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaesong
Context triple: [Mongol invasion of the Korean Peninsula, notableLocation, Kaesong]
  • A. Kaesong chosen
    Kaesong is a historic city in present-day North Korea that served as the capital of the medieval Korean kingdom of Goryeo and remains known for its cultural heritage and traditional architecture.
  • B. Hanseong
    Hanseong was the historical name for Seoul when it served as the capital of the Joseon Dynasty in Korea.
  • C. Kim Chaek City
    Kim Chaek City is an industrial port city in North Hamgyong Province, North Korea, named in honor of the Korean War general and politician Kim Chaek.
  • D. Taebong
    Taebong was a short-lived Korean kingdom of the early 10th century that emerged during the Later Three Kingdoms period before being absorbed by Goryeo.
  • E. Sinchon
    Sinchon is a vibrant university district in Seoul, South Korea, known for its dense concentration of colleges, youth culture, shopping, and nightlife.
  • 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_69d8279402a88190821ffa39ae15bccf completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de911398f08190be85bc0a8bef6b1b completed April 14, 2026, 7:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb73334c8190a96a92d199c3b101 completed May 8, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:18 a.m.