Triple
T13362975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kenya National Park |
E318863
|
entity |
| Predicate | nearestCity |
P350
|
FINISHED |
| Object | Nanyuki |
E324393
|
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: Nanyuki | Statement: [Mount Kenya National Park, nearestCity, Nanyuki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanyuki Context triple: [Mount Kenya National Park, nearestCity, Nanyuki]
-
A.
Nanyuki
chosen
Nanyuki is a Kenyan town on the equator that serves as a popular gateway to Mount Kenya and the surrounding highland wilderness.
-
B.
Nyamira
Nyamira is a town in western Kenya that serves as an administrative and commercial center in the former Nyanza region.
-
C.
Kirinyaga
Kirinyaga is the traditional name used by the Kikuyu people for Mount Kenya, reflecting its cultural and spiritual significance as the sacred mountain of brightness.
-
D.
Naivasha
Naivasha is a town in Kenya’s Rift Valley region known as a gateway to the nearby Lake Naivasha and surrounding wildlife and flower-farming areas.
-
E.
Nyanga
Nyanga is a town and popular tourist destination in eastern Zimbabwe, known for its scenic highlands, national park, and proximity to major waterfalls and mountain landscapes.
- 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_69d806b7bbac8190b85278c87fa7aff3 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69da628affd081909f1790d333f0eef4 |
completed | April 11, 2026, 3:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7397b871c819081272c48b3210e00 |
completed | May 3, 2026, 12:03 p.m. |
Created at: April 9, 2026, 9:32 p.m.