Triple
T15112497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rwanda Ministry of Health |
E360946
|
entity |
| Predicate | headquartersLocation |
P62
|
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: [Rwanda Ministry of Health, headquartersLocation, Kigali]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kigali Context triple: [Rwanda Ministry of Health, headquartersLocation, 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.
Bujumbura
Bujumbura is the largest city and former capital of Burundi, located on the northeastern shore of Lake Tanganyika.
-
C.
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.
-
D.
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.
-
E.
Gitega
Gitega is the political and administrative capital city of Burundi, located in the central part of the country.
- 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_69d85a0491ec8190830960be8fafb994 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0058d786c8190937c6819255c01bd |
completed | April 15, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69febfe2369881908c7ebbad412d9000 |
completed | May 9, 2026, 5:02 a.m. |
Created at: April 10, 2026, 3:05 a.m.