Triple

T22309582
Position Surface form Disambiguated ID Type / Status
Subject Hongdae E551476 entity
Predicate hasLandmark P105 FINISHED
Object Hongik University 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: Hongik University | Statement: [Hongdae, hasLandmark, Hongik University]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hongik University
Context triple: [Hongdae, hasLandmark, Hongik University]
  • A. Hongik University chosen
    Hongik University is a prominent private university in Seoul, South Korea, renowned for its leading fine arts and design programs and its vibrant surrounding youth and arts culture.
  • B. Sejong University
    Sejong University is a private research university in Seoul, South Korea, known for its strong programs in hospitality, tourism, animation, and engineering.
  • C. Chosun University
    Chosun University is a major private research university in South Korea known for its comprehensive academic programs and regional influence.
  • D. Dongguk University
    Dongguk University is a prominent private university in South Korea known for its Buddhist foundation and strong programs in the humanities, arts, and social sciences.
  • E. Kyonggi University
    Kyonggi University is a private South Korean university known for its main campus in Suwon and a broad range of undergraduate and graduate programs.
  • 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.