Triple
T654926
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Nyangani |
E11626
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | town of Nyanga |
E64970
|
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: town of Nyanga | Statement: [Mount Nyangani, near, town of Nyanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: town of Nyanga Context triple: [Mount Nyangani, near, town of Nyanga]
-
A.
Mwinilunga District
Mwinilunga District is a rural district in northwestern Zambia known for its lush forests, high rainfall, and location near the headwaters of major rivers including the Zambezi.
-
B.
Nyanga Highlands
chosen
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
-
C.
Rachuonyo District
Rachuonyo District was a former administrative district in Nyanza Province in western Kenya, known as the birthplace of Barack Obama Sr.
-
D.
Kibondo
Kibondo is a town in western Tanzania that serves as an administrative and commercial center in the Kigoma Region.
-
E.
Negombo
Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49f4bb5b881908a18b5ec1c94e0cf |
completed | March 1, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a591486b708190b0191e958c6c8851 |
completed | March 2, 2026, 1:31 p.m. |
Created at: March 1, 2026, 7:36 p.m.