Triple
T22837396
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heo |
E565983
|
entity |
| Predicate | isFamilyNameOf |
P18
|
FINISHED |
| Object |
Heo Jun
Heo Jun was a prominent 16th-century Korean royal physician and scholar best known for compiling the influential medical text "Dongui Bogam."
|
E1561198
|
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: Heo Jun | Statement: [Heo, isFamilyNameOf, Heo Jun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Heo Jun Context triple: [Heo, isFamilyNameOf, Heo Jun]
-
A.
Heo Jeong
Heo Jeong was a South Korean politician who served as prime minister and played a significant role in the country’s early post-war democratic politics.
-
B.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
C.
Heo Eung-soo
Heo Eung-soo was a South Korean entrepreneur best known as the founder of the LG Group, one of the country’s largest and most influential conglomerates.
-
D.
Jeong Seon
Jeong Seon was a prominent Korean painter of the Joseon dynasty, renowned for pioneering a distinctive style of true-view landscape painting based on actual Korean scenery.
-
E.
Suh Jin-suk
Suh Jin-suk is a notable individual who prominently bears the Korean surname Suh.
- 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: Heo Jun Triple: [Heo, isFamilyNameOf, Heo Jun]
Generated description
Heo Jun was a prominent 16th-century Korean royal physician and scholar best known for compiling the influential medical text "Dongui Bogam."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Heo Jun Target entity description: Heo Jun was a prominent 16th-century Korean royal physician and scholar best known for compiling the influential medical text "Dongui Bogam."
-
A.
Heo Jeong
Heo Jeong was a South Korean politician who served as prime minister and played a significant role in the country’s early post-war democratic politics.
-
B.
Jung Jang-seon
Jung Jang-seon is a South Korean politician serving as the mayor of the city of Pyeongtaek.
-
C.
Heo Eung-soo
Heo Eung-soo was a South Korean entrepreneur best known as the founder of the LG Group, one of the country’s largest and most influential conglomerates.
-
D.
Jeong Seon
Jeong Seon was a prominent Korean painter of the Joseon dynasty, renowned for pioneering a distinctive style of true-view landscape painting based on actual Korean scenery.
-
E.
Suh Jin-suk
Suh Jin-suk is a notable individual who prominently bears the Korean surname Suh.
- 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_69e245869e188190a196584f36e682da |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17e303cec81909c5c118dc8c93354 |
completed | April 29, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bb9b1cf30819095b6d4a158ef21d6 |
completed | May 19, 2026, 1:15 a.m. |
| NEDg | Description generation | batch_6a0bbcfdbe6481909de6da442112803f |
completed | May 19, 2026, 1:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bbd7c3a30819082fdb9cdebca881e |
completed | May 19, 2026, 1:31 a.m. |
Created at: April 17, 2026, 3:35 p.m.