Triple
T16428006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kicukiro District |
E398994
|
entity |
| Predicate | hasUrbanArea |
P316
|
FINISHED |
| Object |
Masaka sector
Masaka sector is an urban administrative sector within Kicukiro District in Kigali, Rwanda.
|
E1217745
|
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: Masaka sector | Statement: [Kicukiro District, hasUrbanArea, Masaka sector]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Masaka sector Context triple: [Kicukiro District, hasUrbanArea, Masaka sector]
-
A.
Gikondo sector
Gikondo sector is an urban administrative area within Kigali, Rwanda, known for its mix of residential neighborhoods, commercial activity, and light industry.
-
B.
Masaka District
Masaka District is an administrative district in southern Uganda known for its agricultural economy and its role as a key transport and commercial hub in the Central Region.
-
C.
Nyarugunga sector
Nyarugunga sector is an urban administrative sector located within Kicukiro District in Kigali, Rwanda.
-
D.
Gikomero sector
Gikomero sector is an administrative subdivision of Gasabo District in Rwanda, encompassing rural communities and local governance structures.
-
E.
Kicukiro sector
Kicukiro sector is an urban administrative sector within Kigali, Rwanda, known for its residential neighborhoods, commercial activity, and proximity to key city infrastructure.
- 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: Masaka sector Triple: [Kicukiro District, hasUrbanArea, Masaka sector]
Generated description
Masaka sector is an urban administrative sector within Kicukiro District in Kigali, Rwanda.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Masaka sector Target entity description: Masaka sector is an urban administrative sector within Kicukiro District in Kigali, Rwanda.
-
A.
Gikondo sector
Gikondo sector is an urban administrative area within Kigali, Rwanda, known for its mix of residential neighborhoods, commercial activity, and light industry.
-
B.
Masaka District
Masaka District is an administrative district in southern Uganda known for its agricultural economy and its role as a key transport and commercial hub in the Central Region.
-
C.
Nyarugunga sector
Nyarugunga sector is an urban administrative sector located within Kicukiro District in Kigali, Rwanda.
-
D.
Gikomero sector
Gikomero sector is an administrative subdivision of Gasabo District in Rwanda, encompassing rural communities and local governance structures.
-
E.
Kicukiro sector
Kicukiro sector is an urban administrative sector within Kigali, Rwanda, known for its residential neighborhoods, commercial activity, and proximity to key city infrastructure.
- 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.