Triple
T196614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Korea |
E3830
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
|
E27919
|
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: Daegu | Statement: [South Korea, hasCity, Daegu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daegu Context triple: [South Korea, hasCity, Daegu]
-
A.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
B.
Busan
Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
-
C.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
D.
Pyongyang
Pyongyang is the capital and largest city of North Korea, serving as its political, economic, and cultural center.
-
E.
Hanyang
Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
- 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: Daegu Triple: [South Korea, hasCity, Daegu]
Generated description
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Daegu Target entity description: Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
A.
Incheon
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
B.
Busan
Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
-
C.
Seoul
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
D.
Pyongyang
Pyongyang is the capital and largest city of North Korea, serving as its political, economic, and cultural center.
-
E.
Hanyang
Hanyang is a historic district and former city now incorporated into Wuhan in Hubei Province, China, known for its early industrial development and strategic location at the confluence of the Han and Yangtze rivers.
- 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a2598594388190a56f36fa036eac84 |
completed | Feb. 28, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a34764cb5c8190b9095a38866387d9 |
completed | Feb. 28, 2026, 7:52 p.m. |
| NEDg | Description generation | batch_69a348107f8c81908102ecab4fafbffe |
completed | Feb. 28, 2026, 7:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a3485cf79881909e9576240f7408a5 |
completed | Feb. 28, 2026, 7:56 p.m. |
Created at: Feb. 28, 2026, 2:41 a.m.