Triple

T5036393
Position Surface form Disambiguated ID Type / Status
Subject Gojoseon E113433 entity
Predicate founder P104 FINISHED
Object Dangun
Dangun is the legendary progenitor of the Korean nation, traditionally revered as the mythical king who established Korea’s first kingdom in ancient times.
 E489919 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: Dangun | Statement: [Gojoseon, founder, Dangun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dangun
Context triple: [Gojoseon, founder, Dangun]
  • A. Gwangalli
    Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
  • B. Seogwipo
    Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
  • C. Donggureung
    Donggureung is a large royal burial complex in Guri, South Korea, containing multiple tombs of Joseon Dynasty kings and queens and recognized as part of a UNESCO World Heritage site.
  • D. Buk-gu
    Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
  • E. Hwaseong
    Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
  • 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: Dangun
Triple: [Gojoseon, founder, Dangun]
Generated description
Dangun is the legendary progenitor of the Korean nation, traditionally revered as the mythical king who established Korea’s first kingdom in ancient times.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dangun
Target entity description: Dangun is the legendary progenitor of the Korean nation, traditionally revered as the mythical king who established Korea’s first kingdom in ancient times.
  • A. Gwangalli
    Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
  • B. Seogwipo
    Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
  • C. Donggureung
    Donggureung is a large royal burial complex in Guri, South Korea, containing multiple tombs of Joseon Dynasty kings and queens and recognized as part of a UNESCO World Heritage site.
  • D. Buk-gu
    Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
  • E. Hwaseong
    Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
  • 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_69bd44384298819089c49e7c330ec7b8 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd73bb069c8190af86f1b2f95f3d95 completed March 20, 2026, 4:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea479f01c8190a84ff4973845eb17 completed March 21, 2026, 2 p.m.
NEDg Description generation batch_69bea525d9088190b0b655687dd27630 completed March 21, 2026, 2:03 p.m.
NED2 Entity disambiguation (via description) batch_69bea594850881909cd683670b63a079 completed March 21, 2026, 2:05 p.m.
Created at: March 20, 2026, 1:37 p.m.