Triple
T14886544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jongno District |
E350135
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Gahoe-dong |
E569706
|
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: Gahoe-dong | Statement: [Jongno District, contains, Gahoe-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gahoe-dong Context triple: [Jongno District, contains, Gahoe-dong]
-
A.
Gahoe-dong
chosen
Gahoe-dong is a historic neighborhood in central Seoul, South Korea, known for its traditional hanok houses and cultural heritage sites.
-
B.
Hwagok-dong
Hwagok-dong is a neighborhood in western Seoul, South Korea, known as a residential and commercial area within Gangseo-gu.
-
C.
Yongho-dong
Yongho-dong is a neighborhood in Busan, South Korea, known as a coastal residential area within the city's southern region.
-
D.
Hwanghak-dong
Hwanghak-dong is a neighborhood in central Seoul, South Korea, known for its traditional flea markets and dense urban streetscape.
-
E.
Banghwa-dong
Banghwa-dong is a neighborhood in Seoul, South Korea, known for its residential character and proximity to Gimpo International Airport.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5f5b1c88190815f3585770cb135 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3aeb59c8190a39ccb4df7815ed0 |
completed | May 9, 2026, 11:30 p.m. |
Created at: April 10, 2026, 1:56 a.m.