Triple
T10300289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anyang |
E241609
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Dongan-gu
Dongan-gu is an urban district of Anyang in Gyeonggi Province, South Korea, known for its residential neighborhoods, commercial centers, and proximity to Seoul.
|
E888379
|
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: Dongan-gu | Statement: [Anyang, hasSubdivision, Dongan-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongan-gu Context triple: [Anyang, hasSubdivision, Dongan-gu]
-
A.
Dong-gu
Dong-gu is a central district of Busan, South Korea, known for its mix of historic neighborhoods, port-related facilities, and major transportation hubs.
-
B.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
C.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
D.
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
-
E.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
- 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: Dongan-gu Triple: [Anyang, hasSubdivision, Dongan-gu]
Generated description
Dongan-gu is an urban district of Anyang in Gyeonggi Province, South Korea, known for its residential neighborhoods, commercial centers, and proximity to Seoul.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dongan-gu Target entity description: Dongan-gu is an urban district of Anyang in Gyeonggi Province, South Korea, known for its residential neighborhoods, commercial centers, and proximity to Seoul.
-
A.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
B.
Dong-gu
Dong-gu is a central district of Busan, South Korea, known for its mix of historic neighborhoods, port-related facilities, and major transportation hubs.
-
C.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
D.
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
-
E.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2eefe8881908a672c4dca7657ca |
completed | April 7, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb028c0788190ae8d6750f2f9634e |
completed | April 14, 2026, 9:22 p.m. |
| NEDg | Description generation | batch_69deb384fb588190ae5d11a60fec0f53 |
completed | April 14, 2026, 9:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69deb4a2d4c48190a828262b1cc05b37 |
completed | April 14, 2026, 9:41 p.m. |
Created at: April 6, 2026, 11:44 a.m.