Triple
T14762573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seodaemun-gu |
E346906
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object |
Mapo-gu
Mapo-gu is a district in western Seoul, South Korea, known for its vibrant Hongdae area, cultural venues, and riverside parks along the Han River.
|
E1141054
|
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: Mapo-gu | Statement: [Seodaemun-gu, hasNeighbour, Mapo-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mapo-gu Context triple: [Seodaemun-gu, hasNeighbour, Mapo-gu]
-
A.
Jung-gu
Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
-
B.
Jung-gu
Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
-
C.
Jung-gu
Jung-gu is a central urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
-
D.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
-
E.
Jung-gu
Jung-gu is a central district of Busan, South Korea, known for its historic markets, port-side location, and dense urban commercial areas.
- 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: Mapo-gu Triple: [Seodaemun-gu, hasNeighbour, Mapo-gu]
Generated description
Mapo-gu is a district in western Seoul, South Korea, known for its vibrant Hongdae area, cultural venues, and riverside parks along the Han River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mapo-gu Target entity description: Mapo-gu is a district in western Seoul, South Korea, known for its vibrant Hongdae area, cultural venues, and riverside parks along the Han River.
-
A.
Jung-gu
Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
-
B.
Jung-gu
Jung-gu is a central urban district name used in several major South Korean cities, typically encompassing key commercial, administrative, and cultural areas.
-
C.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
-
D.
Jung-gu
Jung-gu is a central urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
-
E.
Jung-gu
Jung-gu is a central district of Busan, South Korea, known for its historic markets, port-side location, and dense urban commercial areas.
- 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_69d822e8896c819091169882f9b20486 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec7f3a1608190b1b17624003a0c7f |
completed | April 14, 2026, 11:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec86ea50c819083d0bbed4c459041 |
completed | May 9, 2026, 5:38 a.m. |
| NEDg | Description generation | batch_69fec9b5dc18819087d088e094c3c7b4 |
completed | May 9, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69feca0d38088190910dbf4f2538a9d4 |
completed | May 9, 2026, 5:45 a.m. |
Created at: April 10, 2026, 1:30 a.m.