Triple
T13981925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rwenzori Mountains National Park |
E336330
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Kasese |
E332728
|
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: Kasese | Statement: [Rwenzori Mountains National Park, nearestCity, Kasese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kasese Context triple: [Rwenzori Mountains National Park, nearestCity, Kasese]
-
A.
Kasese
chosen
Kasese is a town in western Uganda that serves as a key gateway to Queen Elizabeth National Park and the Rwenzori Mountains.
-
B.
Soroti
Soroti is a town in eastern Uganda that serves as a regional commercial and administrative center.
-
C.
Mbale
Mbale is a major town in eastern Uganda, known as a commercial and administrative center near the slopes of Mount Elgon.
-
D.
Mbarara District
Mbarara District is an administrative district in southwestern Uganda known for its regional commercial center, agricultural activity, and role as a transport hub.
-
E.
Rubanda District
Rubanda District is an administrative district in southwestern Uganda known for its hilly terrain and rural communities.
- 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_69d81c639e808190a0e4b4f3d31c6a59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2ea10dc88190b9720919a021e570 |
completed | April 14, 2026, 12:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d0008dc819088fcfbfe3640af47 |
completed | May 8, 2026, 1:31 a.m. |
Created at: April 9, 2026, 10:18 p.m.