Triple
T6686579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daegu Gwangyeoksi |
E152111
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
|
E680438
|
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: Dong-gu | Statement: [Daegu Gwangyeoksi, hasDistrict, Dong-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dong-gu Context triple: [Daegu Gwangyeoksi, hasDistrict, Dong-gu]
-
A.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
B.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
C.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
-
D.
Jung-gu
Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
-
E.
Jung-gu
Jung-gu is a central urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
- 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: Dong-gu Triple: [Daegu Gwangyeoksi, hasDistrict, Dong-gu]
Generated description
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dong-gu Target entity description: Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
-
A.
Dong-gu
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
B.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
C.
Jung-gu
Jung-gu is a central urban district of Daegu, South Korea, known for its dense commercial areas, historic sites, and administrative importance.
-
D.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
-
E.
Jung-gu
Jung-gu is a central administrative district of the metropolitan city of Ulsan in South Korea.
- 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_69c89a4282ac8190aa641d466763b786 |
completed | March 29, 2026, 3:19 a.m. |
| NEDg | Description generation | batch_69c89b4795f48190a400f2f278aa203d |
completed | March 29, 2026, 3:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c89f844f1c8190a5a1458dfda9d88d |
completed | March 29, 2026, 3:41 a.m. |
Created at: March 27, 2026, 2:04 p.m.