Triple

T17430346
Position Surface form Disambiguated ID Type / Status
Subject Hungnam E423851 entity
Predicate connectedTo P37 FINISHED
Object Hamhung NE NERFINISHED

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: Hamhung | Statement: [Hungnam, connectedTo, Hamhung]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hamhung
Context triple: [Hungnam, connectedTo, Hamhung]
  • A. Hamhung chosen
    Hamhung is a major city in northeastern North Korea, known as an important industrial center and for its distinctive style of cold noodle dish, Hamhung naengmyeon.
  • B. 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.
  • C. Hanseong
    Hanseong was the historical name for Seoul when it served as the capital of the Joseon Dynasty in Korea.
  • D. Tancheon
    Tancheon is a river in South Korea that flows through the city of Seongnam and serves as a key urban waterway and recreational area.
  • E. Kaesong
    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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d889d88b6081908bada047f5b3ba51 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e448ffb9c8819099fabfeebdc06883 completed April 19, 2026, 3:16 a.m.
Created at: April 10, 2026, 5:46 a.m.