Triple
T6077243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Region |
E135430
|
entity |
| Predicate | hasDepartment |
P35
|
FINISHED |
| Object |
Koung-Khi
Koung-Khi is an administrative department located in the West Region of Cameroon.
|
E573335
|
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: Koung-Khi | Statement: [West Region, hasDepartment, Koung-Khi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Koung-Khi Context triple: [West Region, hasDepartment, Koung-Khi]
-
A.
Ungjin
Ungjin was an ancient city in the Korean kingdom of Baekje that served as one of its historical capitals and a key political and cultural center.
-
B.
Miryang
Miryang is a city in South Gyeongsang Province, South Korea, known for its scenic river valley setting, historical sites, and role as a regional transport and educational hub.
-
C.
Seo-dong
Seo-dong is a neighborhood within Busan’s Geumjeong District in South Korea, known primarily as a residential area with local commerce and community facilities.
-
D.
Jamsil
Jamsil is a neighborhood in southeastern Seoul, South Korea, known for its major sports complexes, large residential areas, and entertainment facilities such as Lotte World.
-
E.
Taebong
Taebong was a short-lived Korean kingdom of the early 10th century that emerged during the Later Three Kingdoms period before being absorbed by Goryeo.
- 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: Koung-Khi Triple: [West Region, hasDepartment, Koung-Khi]
Generated description
Koung-Khi is an administrative department located in the West Region of Cameroon.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Koung-Khi Target entity description: Koung-Khi is an administrative department located in the West Region of Cameroon.
-
A.
Ungjin
Ungjin was an ancient city in the Korean kingdom of Baekje that served as one of its historical capitals and a key political and cultural center.
-
B.
Miryang
Miryang is a city in South Gyeongsang Province, South Korea, known for its scenic river valley setting, historical sites, and role as a regional transport and educational hub.
-
C.
Seo-dong
Seo-dong is a neighborhood within Busan’s Geumjeong District in South Korea, known primarily as a residential area with local commerce and community facilities.
-
D.
Jamsil
Jamsil is a neighborhood in southeastern Seoul, South Korea, known for its major sports complexes, large residential areas, and entertainment facilities such as Lotte World.
-
E.
Taebong
Taebong was a short-lived Korean kingdom of the early 10th century that emerged during the Later Three Kingdoms period before being absorbed by Goryeo.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0576ef2c88190b0ec62e9f041d176 |
completed | March 22, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c14153e95081909e0d77cb48733561 |
completed | March 23, 2026, 1:34 p.m. |
| NEDg | Description generation | batch_69c147512a8081908d7d5fe1b1af8271 |
completed | March 23, 2026, 1:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c147d47ea48190aa888b5185eea9c3 |
completed | March 23, 2026, 2:01 p.m. |
Created at: March 22, 2026, 4:11 p.m.