Triple

T14890938
Position Surface form Disambiguated ID Type / Status
Subject Suwon E359751 entity
Predicate hasUniversity P113 FINISHED
Object Suwon University E1257450 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: Suwon University | Statement: [Suwon, hasUniversity, Suwon University]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Suwon University
Context triple: [Suwon, hasUniversity, Suwon University]
  • A. Sungkyul University
    Sungkyul University is a South Korean higher education institution known for producing alumni such as actor Wi Ha-joon.
  • B. 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.
  • C. Hansung University
    Hansung University is a private higher education institution in Seoul, South Korea, known for its programs in humanities, social sciences, design, and information technology.
  • D. Kyungsung University
    Kyungsung University is a private higher education institution located in Busan, South Korea, known for its programs in humanities, social sciences, arts, and media.
  • E. Kyonggi University chosen
    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 (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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded5f883288190af602633fa7d6860 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0170d8c9f0819099a398814f49f0ed completed May 11, 2026, 6:02 a.m.
Created at: April 10, 2026, 2:10 a.m.