Triple
T6686527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | North Gyeongsang Province |
E152110
|
entity |
| Predicate | hasCounty |
P285
|
FINISHED |
| Object |
Yeongcheon
Yeongcheon is a city in southeastern South Korea known for its agricultural production and historical sites within North Gyeongsang Province.
|
E691757
|
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: Yeongcheon | Statement: [North Gyeongsang Province, hasCounty, Yeongcheon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeongcheon Context triple: [North Gyeongsang Province, hasCounty, Yeongcheon]
-
A.
Jecheon
Jecheon is a city in North Chungcheong Province, South Korea, known as a regional transport hub surrounded by mountains and lakes.
-
B.
Icheon
Icheon is a South Korean city renowned for its traditional ceramics and hot spring resorts.
-
C.
Sangju
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
D.
Namyangju
Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
-
E.
Gyeongbuk
Gyeongbuk is a province in eastern South Korea known for its historical sites, cultural heritage, and scenic rural landscapes.
- 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: Yeongcheon Triple: [North Gyeongsang Province, hasCounty, Yeongcheon]
Generated description
Yeongcheon is a city in southeastern South Korea known for its agricultural production and historical sites within North Gyeongsang Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yeongcheon Target entity description: Yeongcheon is a city in southeastern South Korea known for its agricultural production and historical sites within North Gyeongsang Province.
-
A.
Jecheon
Jecheon is a city in North Chungcheong Province, South Korea, known as a regional transport hub surrounded by mountains and lakes.
-
B.
Icheon
Icheon is a South Korean city renowned for its traditional ceramics and hot spring resorts.
-
C.
Sangju
Sangju is a city in southeastern South Korea known historically for agriculture, particularly rice and dried persimmons, and for its role as a regional transport hub.
-
D.
Namyangju
Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
-
E.
Gyeongbuk
Gyeongbuk is a province in eastern South Korea known for its historical sites, cultural heritage, and scenic rural landscapes.
- 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_69c687f9977c819097e7f5ada4fe522e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b14cd6748190aad4badd5f253478 |
completed | March 27, 2026, 4:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9a9ca03ec8190859d9728fef39d24 |
completed | March 29, 2026, 10:38 p.m. |
| NEDg | Description generation | batch_69c9aa8767448190a98c4ff7c5452a2a |
completed | March 29, 2026, 10:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c9aabb80108190b12939eecab7077d |
completed | March 29, 2026, 10:42 p.m. |
Created at: March 27, 2026, 2:04 p.m.