Triple

T8277859
Position Surface form Disambiguated ID Type / Status
Subject Gojong of Korea E193590 entity
Predicate birthPlace P1 FINISHED
Object Han-seong
Han-seong was the historical name for Korea’s capital city, now known as Seoul, which served as the political and cultural center of the Joseon dynasty.
 E723127 NE FINISHED

How this triple was built (4 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: Han-seong | Statement: [Gojong of Korea, birthPlace, Han-seong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Han-seong
Context triple: [Gojong of Korea, birthPlace, Han-seong]
  • A. Seonghwan
    Seonghwan is a locality in South Korea historically noted as the site of the Battle of Seonghwan during the First Sino-Japanese War.
  • B. Jinhae
    Jinhae is a coastal district in Changwon, South Korea, best known for its large naval base and famous annual cherry blossom festival.
  • C. Lee Tae-hun
    Lee Tae-hun is a South Korean film producer known for his work on the international release of the science fiction thriller "Snowpiercer."
  • D. Jinwicheon
    Jinwicheon is a river flowing through the city of Pyeongtaek in South Korea.
  • E. Ryu Jong-hyun
    Ryu Jong-hyun is a South Korean figure skating coach best known for having coached Olympic champion Yuna Kim earlier in her career.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Han-seong
Triple: [Gojong of Korea, birthPlace, Han-seong]
Generated description
Han-seong was the historical name for Korea’s capital city, now known as Seoul, which served as the political and cultural center of the Joseon dynasty.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Han-seong
Target entity description: Han-seong was the historical name for Korea’s capital city, now known as Seoul, which served as the political and cultural center of the Joseon dynasty.
  • A. Seonghwan
    Seonghwan is a locality in South Korea historically noted as the site of the Battle of Seonghwan during the First Sino-Japanese War.
  • B. Jinhae
    Jinhae is a coastal district in Changwon, South Korea, best known for its large naval base and famous annual cherry blossom festival.
  • C. Lee Tae-hun
    Lee Tae-hun is a South Korean film producer known for his work on the international release of the science fiction thriller "Snowpiercer."
  • D. Jinwicheon
    Jinwicheon is a river flowing through the city of Pyeongtaek in South Korea.
  • E. Ryu Jong-hyun
    Ryu Jong-hyun is a South Korean figure skating coach best known for having coached Olympic champion Yuna Kim earlier in her career.
  • F. None of above. chosen

Provenance (5 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_69ca82e217a48190880695635c44b2ed completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb79ebb6b88190bc777b8bd72fcdbc completed March 31, 2026, 7:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd6863c22c8190b888a23bb9005712 completed April 1, 2026, 6:48 p.m.
NEDg Description generation batch_69cd6d5441248190a9e32281dc8e8d62 completed April 1, 2026, 7:09 p.m.
NED2 Entity disambiguation (via description) batch_69cd7e0d1b8c8190b5183cc176432061 completed April 1, 2026, 8:20 p.m.
Created at: March 30, 2026, 5:51 p.m.