Triple
T1586688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gwangan Bridge |
E34081
|
entity |
| Predicate | connects |
P390
|
FINISHED |
| Object | U-dong |
E227450
|
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: U-dong | Statement: [Gwangan Bridge, connects, U-dong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: U-dong Context triple: [Gwangan Bridge, connects, U-dong]
-
A.
Sasang-dong
Sasang-dong is a neighborhood in Busan, South Korea, known as an urban residential and commercial area within the city's Sasang District.
-
B.
Gaya-dong
Gaya-dong is a neighborhood in Busan, South Korea, known as a residential and commercial area within the central urban zone of the city.
-
C.
Ami-dong
Ami-dong is a neighborhood in Busan, South Korea, known in part for hosting a campus of Pusan National University.
-
D.
Gwangan-dong
chosen
Gwangan-dong is a coastal neighborhood in Busan, South Korea, best known for Gwangalli Beach and its views of the illuminated Gwangan Bridge.
-
E.
Seo-gu
Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
- 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_69a885fceb2c8190b47e0f7c0aefbff0 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a908f3b5f48190bd5eff3ce81c5ffb |
completed | March 5, 2026, 4:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af9040c20c8190aaae6f22fc893032 |
completed | March 10, 2026, 3:30 a.m. |
Created at: March 4, 2026, 7:27 p.m.