Triple
T17811921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheongju |
E444728
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Seowon-gu
Seowon-gu is an administrative district of the city of Cheongju in North Chungcheong Province, South Korea.
|
E1327216
|
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: Seowon-gu | Statement: [Cheongju, hasDistrict, Seowon-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seowon-gu Context triple: [Cheongju, hasDistrict, Seowon-gu]
-
A.
Sujeong-gu
Sujeong-gu is an administrative district in the city of Seongnam, South Korea, known for its residential neighborhoods and proximity to Seoul.
-
B.
Jungwon-gu
Jungwon-gu is an administrative district (gu) of the city of Seongnam in Gyeonggi Province, South Korea, comprising residential, commercial, and cultural areas.
-
C.
Yeonsu-gu
Yeonsu-gu is an administrative district of Incheon, South Korea, known for its coastal location, modern residential areas, and proximity to the Songdo International Business District.
-
D.
Geumjeong-gu
Geumjeong-gu is a district in Busan, South Korea, known for its residential areas, transportation hubs, and proximity to Geumjeongsan Mountain.
-
E.
Seongbuk-gu
Seongbuk-gu is a district in northern Seoul, South Korea, known for its residential neighborhoods, cultural sites, and several major universities.
- 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: Seowon-gu Triple: [Cheongju, hasDistrict, Seowon-gu]
Generated description
Seowon-gu is an administrative district of the city of Cheongju in North Chungcheong Province, South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seowon-gu Target entity description: Seowon-gu is an administrative district of the city of Cheongju in North Chungcheong Province, South Korea.
-
A.
Sujeong-gu
Sujeong-gu is an administrative district in the city of Seongnam, South Korea, known for its residential neighborhoods and proximity to Seoul.
-
B.
Jungwon-gu
Jungwon-gu is an administrative district (gu) of the city of Seongnam in Gyeonggi Province, South Korea, comprising residential, commercial, and cultural areas.
-
C.
Yeonsu-gu
Yeonsu-gu is an administrative district of Incheon, South Korea, known for its coastal location, modern residential areas, and proximity to the Songdo International Business District.
-
D.
Geumjeong-gu
Geumjeong-gu is a district in Busan, South Korea, known for its residential areas, transportation hubs, and proximity to Geumjeongsan Mountain.
-
E.
Seongbuk-gu
Seongbuk-gu is a district in northern Seoul, South Korea, known for its residential neighborhoods, cultural sites, and several major universities.
- 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_69d8b9f0de78819099395b14db75a8a6 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4887b5e50819098506f0b92d709b5 |
completed | April 19, 2026, 7:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a047125683081908a18067ff3fd7956 |
completed | May 13, 2026, 12:40 p.m. |
| NEDg | Description generation | batch_6a04730c241c8190800aa4c99dfa5c08 |
completed | May 13, 2026, 12:48 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0473c5c03c8190956a5d640cfd579d |
completed | May 13, 2026, 12:51 p.m. |
Created at: April 10, 2026, 10:14 a.m.