Triple
T6802724
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sasang-gu Office |
E156223
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object |
Sasang-gu
Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
|
E689213
|
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: Sasang-gu | Statement: [Sasang-gu Office, locatedIn, Sasang-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sasang-gu Context triple: [Sasang-gu Office, locatedIn, Sasang-gu]
-
A.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
B.
Seo-gu
Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
-
C.
Seo-gu
Seo-gu is an administrative district in the city of Daegu, South Korea, known primarily as a residential and commercial urban area.
-
D.
Pusanjin-gu
Pusanjin-gu is a central urban district of Busan, South Korea, known for its major commercial areas, transportation hubs, and dense residential neighborhoods.
-
E.
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.
- 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: Sasang-gu Triple: [Sasang-gu Office, locatedIn, Sasang-gu]
Generated description
Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sasang-gu Target entity description: Sasang-gu is an administrative district in Busan, South Korea, known for its transportation hubs, industrial areas, and mixed residential-commercial neighborhoods.
-
A.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
B.
Seo-gu
Seo-gu is a district of the metropolitan city of Daejeon in South Korea, known for its residential areas, commercial centers, and educational institutions.
-
C.
Seo-gu
Seo-gu is an administrative district in the city of Daegu, South Korea, known primarily as a residential and commercial urban area.
-
D.
Pusanjin-gu
Pusanjin-gu is a central urban district of Busan, South Korea, known for its major commercial areas, transportation hubs, and dense residential neighborhoods.
-
E.
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.
- 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_69c68826e6a48190a3d220b541e639de |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d2e714d4819084c8109c4de7de72 |
completed | March 27, 2026, 6:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8f2ffec108190ab60b0777d97dd89 |
completed | March 29, 2026, 9:38 a.m. |
| NEDg | Description generation | batch_69c8f39c8cdc81908918d6dc5948012e |
completed | March 29, 2026, 9:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8f46ab5ec819092508ddedc816c89 |
completed | March 29, 2026, 9:44 a.m. |
Created at: March 27, 2026, 2:16 p.m.