Triple
T14762582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Seodaemun-gu |
E346906
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Chungjeongno-dong
Chungjeongno-dong is a neighborhood in central Seoul, South Korea, known for its mix of commercial offices, residential areas, and convenient access to major transportation routes.
|
E1153614
|
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: Chungjeongno-dong | Statement: [Seodaemun-gu, contains, Chungjeongno-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chungjeongno-dong Context triple: [Seodaemun-gu, contains, Chungjeongno-dong]
-
A.
Yeocheon-dong
Yeocheon-dong is a neighborhood in Ulsan, South Korea, known for encompassing the expansive Ulsan Grand Park.
-
B.
Cheonyeon-dong
Cheonyeon-dong is a neighborhood (dong) in Seoul, South Korea, known as a residential area within the central-western part of the city.
-
C.
Daecheong-dong
Daecheong-dong is a neighborhood in central Busan, South Korea, known for its urban setting within the city's Jung District.
-
D.
Okryeon-dong
Okryeon-dong is a neighborhood located within Yeonsu District in Incheon, South Korea.
-
E.
Suyeong-dong
Suyeong-dong is a neighborhood in Busan, South Korea, known as part of the urban area within Suyeong District.
- 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: Chungjeongno-dong Triple: [Seodaemun-gu, contains, Chungjeongno-dong]
Generated description
Chungjeongno-dong is a neighborhood in central Seoul, South Korea, known for its mix of commercial offices, residential areas, and convenient access to major transportation routes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Chungjeongno-dong Target entity description: Chungjeongno-dong is a neighborhood in central Seoul, South Korea, known for its mix of commercial offices, residential areas, and convenient access to major transportation routes.
-
A.
Yeocheon-dong
Yeocheon-dong is a neighborhood in Ulsan, South Korea, known for encompassing the expansive Ulsan Grand Park.
-
B.
Cheonyeon-dong
Cheonyeon-dong is a neighborhood (dong) in Seoul, South Korea, known as a residential area within the central-western part of the city.
-
C.
Daecheong-dong
Daecheong-dong is a neighborhood in central Busan, South Korea, known for its urban setting within the city's Jung District.
-
D.
Okryeon-dong
Okryeon-dong is a neighborhood located within Yeonsu District in Incheon, South Korea.
-
E.
Suyeong-dong
Suyeong-dong is a neighborhood in Busan, South Korea, known as part of the urban area within Suyeong District.
- 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_69ff0b3216fc8190b79740a993b98cb3 |
completed | May 9, 2026, 10:23 a.m. |
| NEDg | Description generation | batch_69ff0fa785f88190a65c7cecbc6e0554 |
completed | May 9, 2026, 10:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff107121ac8190af8682dcdcebc893 |
completed | May 9, 2026, 10:46 a.m. |
Created at: April 10, 2026, 1:30 a.m.