Triple

T19881793
Position Surface form Disambiguated ID Type / Status
Subject Namsan E477792 entity
Predicate hasLandmark P105 FINISHED
Object N Seoul Tower E532617 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: N Seoul Tower | Statement: [Namsan, hasLandmark, N Seoul Tower]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: N Seoul Tower
Context triple: [Namsan, hasLandmark, N Seoul Tower]
  • A. N Seoul Tower chosen
    N Seoul Tower is a prominent communication and observation tower on Namsan Mountain that serves as one of Seoul’s most recognizable cityscape landmarks and tourist attractions.
  • B. Kyobo Tower, Seoul
    Kyobo Tower in Seoul is a prominent modern office and commercial building best known as a landmark work of Swiss architect Mario Botta.
  • C. Busan Tower
    Busan Tower is a prominent observation tower in Busan, South Korea, offering panoramic views of the city and its harbor.
  • D. Mount Namsan
    Mount Namsan is a historically significant mountain in Gyeongju, South Korea, renowned for its numerous ancient Buddhist relics, temples, and archaeological sites.
  • E. Jongno Tower
    Jongno Tower is a prominent high-rise office and commercial building in central Seoul, South Korea, known for its distinctive modern architecture and rooftop observatory.
  • 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_6a07dbcce4048190ba3b17c1dc4e1095 completed May 16, 2026, 2:51 a.m.
Created at: April 10, 2026, 1:52 p.m.