Triple
T7996629
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gangdong-dong (district office area) |
E186142
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Gangdong-dong |
E687249
|
NE FINISHED |
How this triple was built (2 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: Gangdong-dong | Statement: [Gangdong-dong (district office area), hasName, Gangdong-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gangdong-dong Context triple: [Gangdong-dong (district office area), hasName, Gangdong-dong]
-
A.
Gangdong-dong
chosen
Gangdong-dong is a neighborhood in Busan, South Korea, serving as the central administrative hub of the city's Gangseo District.
-
B.
Won-dong
Won-dong is a neighborhood (dong) within the city of Osan in Gyeonggi Province, South Korea.
-
C.
Sudogwon
Sudogwon is the Seoul Capital Area of South Korea, encompassing Seoul, Incheon, and surrounding Gyeonggi Province as the country’s largest and most populous metropolitan region.
-
D.
Itaewon
Itaewon is a vibrant multicultural district in Seoul known for its international cuisine, nightlife, and diverse expatriate community.
-
E.
Yangjeong-dong
Yangjeong-dong is a neighborhood (dong) located within Busanjin District in the city of Busan, South Korea.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca829c6c308190ab05b43d234c52b2 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c97968481908d261b3f0bd6b8e6 |
completed | March 31, 2026, 3:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe10d5eb081909f257390094de442 |
completed | March 31, 2026, 2:58 p.m. |
Created at: March 30, 2026, 5:17 p.m.