Triple
T516893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mutare |
E10728
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Nyanga Highlands
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
|
E64970
|
NE FINISHED |
How this triple was built (4 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: Nyanga Highlands | Statement: [Mutare, near, Nyanga Highlands]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nyanga Highlands Context triple: [Mutare, near, Nyanga Highlands]
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
C.
Katavi Region
Katavi Region is a sparsely populated administrative region in western Tanzania known for its vast wilderness areas and the wildlife-rich Katavi National Park.
-
D.
Erg Chigaga
Erg Chigaga is a vast, remote dune field in southern Morocco known for its towering sand dunes and desert wilderness landscapes.
-
E.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nyanga Highlands Triple: [Mutare, near, Nyanga Highlands]
Generated description
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nyanga Highlands Target entity description: Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
-
A.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
B.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
C.
Katavi Region
Katavi Region is a sparsely populated administrative region in western Tanzania known for its vast wilderness areas and the wildlife-rich Katavi National Park.
-
D.
Erg Chigaga
Erg Chigaga is a vast, remote dune field in southern Morocco known for its towering sand dunes and desert wilderness landscapes.
-
E.
Kigoma Region
Kigoma Region is a western Tanzanian administrative region along Lake Tanganyika, known for its biodiversity and as a center for primate research.
- F. None of above. chosen
Provenance (5 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_69a2e84a0d08819087e01863fcd9abf1 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f184c3a481909bf60bb627b0ea88 |
completed | Feb. 28, 2026, 1:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4a6fd69148190abfd945bc5434cf2 |
completed | March 1, 2026, 8:52 p.m. |
| NEDg | Description generation | batch_69a4a884de0c8190b2243403f6bc870b |
completed | March 1, 2026, 8:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a4a925f60c8190aa5b13380a712974 |
completed | March 1, 2026, 9:01 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.