Triple

T22310202
Position Surface form Disambiguated ID Type / Status
Subject Jongno Tower E551490 entity
Predicate hasViewOf P854 FINISHED
Object Namsan Seoul Tower 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: Namsan Seoul Tower | Statement: [Jongno Tower, hasViewOf, Namsan Seoul Tower]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Namsan Seoul Tower
Context triple: [Jongno Tower, hasViewOf, Namsan 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. Mount Namsan
    Mount Namsan is a historically significant mountain in Gyeongju, South Korea, renowned for its numerous ancient Buddhist relics, temples, and archaeological sites.
  • C. 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.
  • D. Busan Tower
    Busan Tower is a prominent observation tower in Busan, South Korea, offering panoramic views of the city and its harbor.
  • E. Trade Tower (Seoul)
    Trade Tower (Seoul) is a prominent skyscraper and major business complex in Seoul’s Gangnam district, known for housing offices, exhibition spaces, and being part of the COEX convention and shopping area.
  • 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_69e11e46c0188190800181a4233f28fe completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f1574d53148190a1ec07f849e1ae9d completed April 29, 2026, 12:56 a.m.
Created at: April 16, 2026, 8:42 p.m.