Triple
T6138970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Senate of Rwanda |
E136909
|
entity |
| Predicate | meetsIn |
P40
|
FINISHED |
| Object | Kigali |
E87281
|
NE FINISHED |
How this triple was built (2 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: Kigali | Statement: [Senate of Rwanda, meetsIn, Kigali]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kigali Context triple: [Senate of Rwanda, meetsIn, Kigali]
-
A.
Kigali
chosen
Kigali is the capital and largest city of Rwanda, known as a major political and economic hub in East Africa.
-
B.
Gisenyi
Gisenyi is a city in northwestern Rwanda on the shores of Lake Kivu, historically significant as one of the key sites affected during the 1994 Rwandan genocide.
-
C.
Bukavu
Bukavu is a major city in the eastern Democratic Republic of the Congo, located on the southwestern shore of Lake Kivu near the Rwandan border.
-
D.
Gitega
Gitega is the political and administrative capital city of Burundi, located in the central part of the country.
-
E.
GOMA
GOMA is a contemporary art museum known for showcasing modern and experimental artworks, often associated with major cultural institutions in cities like Brisbane and Glasgow.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c008a179388190a3b5a081bbf46d55 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c05c855a2481909801de9fd55686a4 |
completed | March 22, 2026, 9:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c16ece259c8190b7258859a35ca4fd |
completed | March 23, 2026, 4:48 p.m. |
Created at: March 22, 2026, 4:15 p.m.