Triple

T6686511
Position Surface form Disambiguated ID Type / Status
Subject North Gyeongsang Province E152110 entity
Predicate hasCity P316 FINISHED
Object Gyeongju E241549 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: Gyeongju | Statement: [North Gyeongsang Province, hasCity, Gyeongju]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gyeongju
Context triple: [North Gyeongsang Province, hasCity, Gyeongju]
  • A. Gyeongju chosen
    Gyeongju is a historic city in South Korea famed for its rich cultural heritage and numerous archaeological sites from the ancient Silla Kingdom.
  • B. Jinju-si
    Jinju-si is a city in South Gyeongsang Province, South Korea, known for its historic Jinju Fortress and the annual Namgang Yudeung (Lantern) Festival.
  • C. Gimhae
    Gimhae is a city in South Gyeongsang Province, South Korea, known for its historical significance as the birthplace of the ancient Gaya confederacy and its proximity to the metropolitan city of Busan.
  • D. Cheongju
    Cheongju is a major city in central South Korea that serves as the capital of North Chungcheong Province and an important regional administrative, educational, and transportation hub.
  • E. Sejong City
    Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
  • 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_69c687f9977c819097e7f5ada4fe522e completed March 27, 2026, 1:36 p.m.
NER Named-entity recognition batch_69c6b14cd6748190aad4badd5f253478 completed March 27, 2026, 4:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c998a68c24819099abe74525830812 completed March 29, 2026, 9:24 p.m.
Created at: March 27, 2026, 2:04 p.m.