Triple
T16428030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kicukiro District |
E398994
|
entity |
| Predicate | hasSector |
P71
|
FINISHED |
| Object |
Gikondo
Gikondo is an urban sector of Kigali, Rwanda, known for its industrial area and proximity to the city center.
|
E1217746
|
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: Gikondo | Statement: [Kicukiro District, hasSector, Gikondo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gikondo Context triple: [Kicukiro District, hasSector, Gikondo]
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
C.
Butiama
Butiama is a village in northern Tanzania best known as the birthplace and hometown of the country’s founding president, Julius Nyerere.
-
D.
Bukoba
Bukoba is a town on the western shore of Lake Victoria in northwestern Tanzania, serving as the capital of the Kagera Region and a local transport and trade hub.
-
E.
Mbulu
Mbulu is an ethnic group in northern Tanzania, more commonly known as the Iraqw people, noted for their Cushitic language and intensive agriculture.
- 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: Gikondo Triple: [Kicukiro District, hasSector, Gikondo]
Generated description
Gikondo is an urban sector of Kigali, Rwanda, known for its industrial area and proximity to the city center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gikondo Target entity description: Gikondo is an urban sector of Kigali, Rwanda, known for its industrial area and proximity to the city center.
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
C.
Butiama
Butiama is a village in northern Tanzania best known as the birthplace and hometown of the country’s founding president, Julius Nyerere.
-
D.
Bukoba
Bukoba is a town on the western shore of Lake Victoria in northwestern Tanzania, serving as the capital of the Kagera Region and a local transport and trade hub.
-
E.
Mbulu
Mbulu is an ethnic group in northern Tanzania, more commonly known as the Iraqw people, noted for their Cushitic language and intensive agriculture.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328fc223c8190bbed29907351a6f6 |
completed | April 18, 2026, 6:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a006071a8ac8190b004a2861343960a |
completed | May 10, 2026, 10:39 a.m. |
| NEDg | Description generation | batch_6a0060fd5d6c819099d5d1ccaaa907c9 |
completed | May 10, 2026, 10:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0061e78b68819095d554b2ff7a329a |
completed | May 10, 2026, 10:45 a.m. |
Created at: April 10, 2026, 5:09 a.m.