Triple
T23123951
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Gahinga |
E576973
|
entity |
| Predicate | nearSettlement |
P3883
|
FINISHED |
| Object | Kisoro |
—
|
NE NERFINISHED |
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: Kisoro | Statement: [Mount Gahinga, nearSettlement, Kisoro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kisoro Context triple: [Mount Gahinga, nearSettlement, Kisoro]
-
A.
Kisoro
chosen
Kisoro is a small town in southwestern Uganda known as a gateway to gorilla trekking and the nearby Bwindi Impenetrable and Mgahinga Gorilla National Parks.
-
B.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
C.
Nakasongola
Nakasongola is a town in central Uganda that serves as an administrative and commercial center for the surrounding rural district.
-
D.
Gikondo
Gikondo is an urban sector of Kigali, Rwanda, known for its industrial area and proximity to the city center.
-
E.
Isiburu
Isiburu is a novel by Nigerian writer Elechi Amadi that explores themes of tradition, honor, and personal conflict within an African village setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245f6c2e881909a228fdcfeb7c7d3 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e5299c08190a578102cf2ff7080 |
completed | April 29, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:59 p.m.