Triple
T9793752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyanza region |
E237667
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Migori |
E814175
|
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: Migori | Statement: [Nyanza region, hasTown, Migori]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Migori Context triple: [Nyanza region, hasTown, Migori]
-
A.
Migori County
chosen
Migori County is an administrative region in southwestern Kenya known for its diverse ethnic communities, agriculture, and proximity to Lake Victoria and the Tanzanian border.
-
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.
Kisumu County
Kisumu County is a county in western Kenya along Lake Victoria, known as a major economic and political hub and the location of the city of Kisumu.
-
D.
Nyamira County
Nyamira County is an administrative county in western Kenya known for its predominantly Kisii community, hilly highland terrain, and tea and coffee farming.
-
E.
Narok County
Narok County is a county in southwestern Kenya known for its vast savannah landscapes, rich Maasai culture, and world-famous wildlife 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_69ca84dc04488190b9c91193976c0960 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda347b6bc8190a99b7dec1650cd46 |
completed | April 1, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1cc5118a481908a65d730f86c7723 |
completed | April 5, 2026, 2:43 a.m. |
Created at: March 30, 2026, 8:28 p.m.