Triple
T9413079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uiwang |
E226750
|
entity |
| Predicate | hasLake |
P1025
|
FINISHED |
| Object |
Baegun Lake
Baegun Lake is a scenic artificial reservoir and recreational area located in the city of Uiwang, South Korea.
|
E803676
|
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: Baegun Lake | Statement: [Uiwang, hasLake, Baegun Lake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baegun Lake Context triple: [Uiwang, hasLake, Baegun Lake]
-
A.
Suseong Lake
Suseong Lake is a popular recreational lake in Daegu, South Korea, known for its scenic walking paths, cafes, and cultural events.
-
B.
Đại Lải Lake
Đại Lải Lake is a popular scenic reservoir and resort area in northern Vietnam known for its tranquil waters, surrounding pine forests, and recreational activities.
-
C.
Majang Lake
Majang Lake is a scenic reservoir and leisure destination in Paju, South Korea, known for its walking trails, lakeside cafes, and tranquil natural surroundings.
-
D.
Chundangji Pond
Chundangji Pond is a scenic historic pond and garden feature located within Changgyeonggung Palace in Seoul, South Korea.
-
E.
Maota Lake
Maota Lake is a historic artificial lake in Jaipur, India, known for its scenic setting below the Amber Fort and its role as a former water source for the fort complex.
- 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: Baegun Lake Triple: [Uiwang, hasLake, Baegun Lake]
Generated description
Baegun Lake is a scenic artificial reservoir and recreational area located in the city of Uiwang, South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baegun Lake Target entity description: Baegun Lake is a scenic artificial reservoir and recreational area located in the city of Uiwang, South Korea.
-
A.
Suseong Lake
Suseong Lake is a popular recreational lake in Daegu, South Korea, known for its scenic walking paths, cafes, and cultural events.
-
B.
Đại Lải Lake
Đại Lải Lake is a popular scenic reservoir and resort area in northern Vietnam known for its tranquil waters, surrounding pine forests, and recreational activities.
-
C.
Majang Lake
Majang Lake is a scenic reservoir and leisure destination in Paju, South Korea, known for its walking trails, lakeside cafes, and tranquil natural surroundings.
-
D.
Chundangji Pond
Chundangji Pond is a scenic historic pond and garden feature located within Changgyeonggung Palace in Seoul, South Korea.
-
E.
Maota Lake
Maota Lake is a historic artificial lake in Jaipur, India, known for its scenic setting below the Amber Fort and its role as a former water source for the fort complex.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5258f7e081908d48600409181fdb |
completed | April 1, 2026, 5:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d139ce08ec81908a8e81c060667ac3 |
completed | April 4, 2026, 4:18 p.m. |
| NEDg | Description generation | batch_69d13a9cd1848190a31730405bb78fcd |
completed | April 4, 2026, 4:21 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d13af5a7808190a216d4fc8c44c8b1 |
completed | April 4, 2026, 4:23 p.m. |
Created at: March 30, 2026, 7:47 p.m.