Triple

T19881819
Position Surface form Disambiguated ID Type / Status
Subject Namsan E477792 entity
Predicate locatedNear P294 FINISHED
Object Itaewon E109253 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: Itaewon | Statement: [Namsan, locatedNear, Itaewon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Itaewon
Context triple: [Namsan, locatedNear, Itaewon]
  • A. Itaewon chosen
    Itaewon is a vibrant multicultural district in Seoul known for its international cuisine, nightlife, and diverse expatriate community.
  • B. Hongje-dong
    Hongje-dong is a neighborhood in western Seoul, South Korea, known as a residential and commercial area within Seodaemun District.
  • C. Hongdae
    Hongdae is a vibrant neighborhood in Seoul known for its indie music scene, street art, nightlife, and youth culture centered around Hongik University.
  • D. Myeongdong
    Myeongdong is a major shopping and entertainment district in central Seoul, famous for its fashion boutiques, street food, and vibrant nightlife.
  • E. Songdo-dong
    Songdo-dong is a modern waterfront neighborhood in Incheon, South Korea, best known for hosting the high-tech, master-planned Songdo International Business District.
  • 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_69d8e51f32b08190b3687f4f60353250 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658df3f5c81909b5b290de91b8d50 completed April 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a07fdbbf76481908ec131f3dd4067aa completed May 16, 2026, 5:16 a.m.
Created at: April 10, 2026, 1:52 p.m.