Triple
T11137562
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kericho District |
E263453
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object |
Kericho
Kericho is a town in western Kenya renowned as a major center of the country’s tea-growing industry.
|
E263453
|
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: Kericho | Statement: [Kericho District, containsSettlement, Kericho]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kericho Context triple: [Kericho District, containsSettlement, Kericho]
-
A.
Eldoret
Eldoret is a major town in western Kenya known as an agricultural and commercial hub and as a center for world-class long-distance runners.
-
B.
Kericho District
Kericho District was a former administrative district in Kenya’s Rift Valley Province, known primarily for its extensive tea plantations and cool highland climate.
-
C.
Kabete
Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
-
D.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
E.
Nakuru
Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural and commercial center.
- 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: Kericho Triple: [Kericho District, containsSettlement, Kericho]
Generated description
Kericho is a town in western Kenya renowned as a major center of the country’s tea-growing industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kericho Target entity description: Kericho is a town in western Kenya renowned as a major center of the country’s tea-growing industry.
-
A.
Eldoret
Eldoret is a major town in western Kenya known as an agricultural and commercial hub and as a center for world-class long-distance runners.
-
B.
Kericho District
chosen
Kericho District was a former administrative district in Kenya’s Rift Valley Province, known primarily for its extensive tea plantations and cool highland climate.
-
C.
Kabete
Kabete is a prominent town in Kenya’s Central Region, situated within Kiambu County and known for its agricultural activity and proximity to Nairobi.
-
D.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
E.
Nakuru
Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural and commercial center.
- F. None of above.
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_69d6aa9c0ba08190bbd19c217489b755 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e85f2ea48190bf1ff63af1d7d236 |
completed | April 9, 2026, 5:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e463177bfc81908a3e66ffc0777777 |
completed | April 19, 2026, 5:07 a.m. |
| NEDg | Description generation | batch_69e4666f98ac81908b3d3b8a6a8af8c9 |
completed | April 19, 2026, 5:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e46c3f28dc8190a521c00151b01fde |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 8, 2026, 9:28 p.m.