Triple
T20796149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daegu |
E511913
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Seo District
Seo District is a central administrative and commercial district of Daegu, South Korea, known for its urban neighborhoods and transportation hubs.
|
E1453264
|
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: Seo District | Statement: [Daegu, hasDistrict, Seo District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seo District Context triple: [Daegu, hasDistrict, Seo District]
-
A.
Seo District
Seo District is a western coastal district of Incheon, South Korea, known for its industrial complexes, port facilities, and growing residential areas.
-
B.
Daowai District
Daowai District is an urban district of Harbin in Heilongjiang Province, China, known for its historic architecture and traditional neighborhoods.
-
C.
Xi District
Xi District is an urban administrative district of the city of Panzhihua in Sichuan Province, China.
-
D.
Moma District
Moma District is an administrative district in the Sakha Republic (Yakutia) in northeastern Russia, known for its remote Arctic landscapes and river valleys.
-
E.
Keifan district
Keifan district is a residential and educational area in Kuwait City known for hosting several university campuses and cultural institutions.
- 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: Seo District Triple: [Daegu, hasDistrict, Seo District]
Generated description
Seo District is a central administrative and commercial district of Daegu, South Korea, known for its urban neighborhoods and transportation hubs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Seo District Target entity description: Seo District is a central administrative and commercial district of Daegu, South Korea, known for its urban neighborhoods and transportation hubs.
-
A.
Seo District
Seo District is a western coastal district of Incheon, South Korea, known for its industrial complexes, port facilities, and growing residential areas.
-
B.
Daowai District
Daowai District is an urban district of Harbin in Heilongjiang Province, China, known for its historic architecture and traditional neighborhoods.
-
C.
Xi District
Xi District is an urban administrative district of the city of Panzhihua in Sichuan Province, China.
-
D.
Moma District
Moma District is an administrative district in the Sakha Republic (Yakutia) in northeastern Russia, known for its remote Arctic landscapes and river valleys.
-
E.
Keifan district
Keifan district is a residential and educational area in Kuwait City known for hosting several university campuses and cultural institutions.
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2ad6f0481909e0bab7119f10f9c |
completed | April 21, 2026, 12:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a090af516888190a8ec410d45f8a65d |
completed | May 17, 2026, 12:25 a.m. |
| NEDg | Description generation | batch_6a090b962adc8190b9a9353acdcc908e |
completed | May 17, 2026, 12:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a090be46044819095f7eeeaba74fb97 |
completed | May 17, 2026, 12:29 a.m. |
Created at: April 16, 2026, 12:39 p.m.