Triple

T23218726
Position Surface form Disambiguated ID Type / Status
Subject Kaeson Street E580822 entity
Predicate hasNameInLanguage P15 FINISHED
Object 개선거리 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: 개선거리 | Statement: [Kaeson Street, hasNameInLanguage, 개선거리]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 개선거리
Context triple: [Kaeson Street, hasNameInLanguage, 개선거리]
  • A. Yongbieocheonga
    Yongbieocheonga is a 15th-century Korean poetic work, regarded as the first piece of literature written in Hangul and celebrating the founding and virtues of the Joseon dynasty.
  • B. Korea Way chosen
    Korea Way is a vibrant stretch of Manhattan known for its dense concentration of Korean restaurants, shops, and cultural businesses, forming the core of New York City's Koreatown.
  • C. Gimje
    Gimje is a city in North Jeolla Province, South Korea, known for its expansive plains and agricultural production, particularly rice.
  • D. Ghindae
    Ghindae is a town in Eritrea located within the Northern Red Sea administrative region.
  • E. Gengxin
    Gengxin is a Chinese given name most notably borne by actor Lin Gengxin, known for his roles in popular Chinese films and television dramas.
  • 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_69e2460389408190be74f41d217799a9 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f191675de48190858907872a065c56 completed April 29, 2026, 5:04 a.m.
Created at: April 17, 2026, 4:08 p.m.