Triple
T1978688
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pyeongtaek |
E42974
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
|
E311006
|
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: Hwaseong | Statement: [Pyeongtaek, borderedBy, Hwaseong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hwaseong Context triple: [Pyeongtaek, borderedBy, Hwaseong]
-
A.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
-
B.
Buk-gu
Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
-
C.
Taejongdae Park
Taejongdae Park is a scenic coastal park in Busan, South Korea, famous for its dramatic seaside cliffs, lighthouse views, and walking trails overlooking the ocean.
-
D.
Gapcheon
Gapcheon is a major river flowing through the city of Daejeon in South Korea, serving as a central natural and recreational landmark.
-
E.
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.
- 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: Hwaseong Triple: [Pyeongtaek, borderedBy, Hwaseong]
Generated description
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hwaseong Target entity description: Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
-
A.
Gwangalli
Gwangalli is a coastal neighborhood in Busan, South Korea, best known for its sandy beach, vibrant nightlife, and scenic views of the nearby Gwangan Bridge.
-
B.
Buk-gu
Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
-
C.
Taejongdae Park
Taejongdae Park is a scenic coastal park in Busan, South Korea, famous for its dramatic seaside cliffs, lighthouse views, and walking trails overlooking the ocean.
-
D.
Gapcheon
Gapcheon is a major river flowing through the city of Daejeon in South Korea, serving as a central natural and recreational landmark.
-
E.
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.
- 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_69a8871289048190b00b0d7744b7b2b1 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb43011188190b6a41c004e9e4802 |
completed | March 7, 2026, 5:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b08609a50c81908b258bc34996ede1 |
completed | March 10, 2026, 8:58 p.m. |
| NEDg | Description generation | batch_69b0d066b9388190ab7ae362ec8ac9f3 |
completed | March 11, 2026, 2:16 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0d0ca37d88190a1c0605f7df688d0 |
completed | March 11, 2026, 2:17 a.m. |
Created at: March 4, 2026, 7:36 p.m.