Triple
T1655498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Geumjeong District |
E35789
|
entity |
| Predicate | romanization |
P2508
|
FINISHED |
| Object |
Kŭmchŏng-gu
Kŭmchŏng-gu is the McCune–Reischauer romanization of Geumjeong District, an administrative district in Busan, South Korea.
|
E296748
|
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: Kŭmchŏng-gu | Statement: [Geumjeong District, romanization, Kŭmchŏng-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kŭmchŏng-gu Context triple: [Geumjeong District, romanization, Kŭmchŏng-gu]
-
A.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
B.
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.
-
C.
Suyŏng-gu
Suyŏng-gu is an urban district of Busan, South Korea, known for its coastal location and role as a residential and commercial hub within the city.
-
D.
Geumwang-eup
Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
-
E.
Daedeok-gu
Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
- 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: Kŭmchŏng-gu Triple: [Geumjeong District, romanization, Kŭmchŏng-gu]
Generated description
Kŭmchŏng-gu is the McCune–Reischauer romanization of Geumjeong District, an administrative district in Busan, South Korea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kŭmchŏng-gu Target entity description: Kŭmchŏng-gu is the McCune–Reischauer romanization of Geumjeong District, an administrative district in Busan, South Korea.
-
A.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
B.
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.
-
C.
Suyŏng-gu
Suyŏng-gu is an urban district of Busan, South Korea, known for its coastal location and role as a residential and commercial hub within the city.
-
D.
Geumwang-eup
Geumwang-eup is a town-level administrative division in Eumseong County, located in North Chungcheong Province, South Korea.
-
E.
Daedeok-gu
Daedeok-gu is a district in the city of Daejeon, South Korea, known for encompassing parts of the country’s major research and science complex.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a8b597c81908a62b41718d85df6 |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc01252ec8190a14ff51151d8e69e |
completed | March 10, 2026, 6:54 a.m. |
| NEDg | Description generation | batch_69afc0d32d5881908b80e0bfca5cd873 |
completed | March 10, 2026, 6:57 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc133f8088190bd505db0d0d1d6f7 |
completed | March 10, 2026, 6:59 a.m. |
Created at: March 4, 2026, 7:29 p.m.