Triple
T14998647
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kajiado County |
E374024
|
entity |
| Predicate | majorTown |
P316
|
FINISHED |
| Object | Ngong |
E773374
|
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: Ngong | Statement: [Kajiado County, majorTown, Ngong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ngong Context triple: [Kajiado County, majorTown, Ngong]
-
A.
Ngong
chosen
Ngong is a town on the outskirts of Nairobi in Kenya, known for the nearby Ngong Hills and its scenic views over the Great Rift Valley.
-
B.
Murang’a
Murang’a is a town in central Kenya that serves as an important commercial and cultural hub in a region historically associated with the Kikuyu community.
-
C.
Mhangura
Mhangura is a small mining town in northern Zimbabwe known historically for its copper production.
-
D.
Nanyuki
Nanyuki is a Kenyan town on the equator that serves as a popular gateway to Mount Kenya and the surrounding highland wilderness.
-
E.
Chyulu Hills
Chyulu Hills is a volcanic mountain range in southeastern Kenya known for its dramatic lava flows, rolling green hills, and rich wildlife within a protected conservation area.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded71a5618819083ae96a79735ef98 |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe969c3ba88190899f06b185e94ccf |
completed | May 9, 2026, 2:06 a.m. |
Created at: April 10, 2026, 2:54 a.m.