Triple
T6846947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gubongsan |
E157918
|
entity |
| Predicate | hasViewOf |
P854
|
FINISHED |
| Object | Daejeon cityscape |
E28250
|
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: Daejeon cityscape | Statement: [Gubongsan, hasViewOf, Daejeon cityscape]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daejeon cityscape Context triple: [Gubongsan, hasViewOf, Daejeon cityscape]
-
A.
Daegu, South Korea
Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
-
B.
Daejeon
chosen
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
C.
Jeonju, South Korea
Jeonju, South Korea is a historic city renowned for its well-preserved Hanok Village, traditional Korean culture, and as the birthplace of the dish bibimbap.
-
D.
Daedeok Science Town
Daedeok Science Town is a major research and development hub in South Korea, known for its concentration of high-tech industries, government research institutes, and universities.
-
E.
Dongducheon
Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
- 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_69c6882ed4c081909dc465a7cf8838be |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d7cd0e64819097c9c211df8bce54 |
completed | March 27, 2026, 7:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72fc931d881908661483836cc059e |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:20 p.m.