Triple
T8941333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiambu County |
E212906
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object |
Limuru
Limuru is a highland town in central Kenya known for its cool climate, tea plantations, and proximity to Nairobi.
|
E769444
|
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: Limuru | Statement: [Kiambu County, hasMajorTown, Limuru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limuru Context triple: [Kiambu County, hasMajorTown, Limuru]
-
A.
Moshi
Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
-
B.
Malindi
Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
-
C.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
D.
Massinga
Massinga is a coastal town in southern Mozambique that serves as an important local center within Inhambane Province.
-
E.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
- 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: Limuru Triple: [Kiambu County, hasMajorTown, Limuru]
Generated description
Limuru is a highland town in central Kenya known for its cool climate, tea plantations, and proximity to Nairobi.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Limuru Target entity description: Limuru is a highland town in central Kenya known for its cool climate, tea plantations, and proximity to Nairobi.
-
A.
Moshi
Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
-
B.
Malindi
Malindi is a historic coastal town in southeastern Kenya known for its beaches, Swahili culture, and role as a former trading port on the Indian Ocean.
-
C.
Maswa
Maswa is a town and administrative district in northern Tanzania, known for its agricultural activities within the Simiyu Region.
-
D.
Massinga
Massinga is a coastal town in southern Mozambique that serves as an important local center within Inhambane Province.
-
E.
Kumba
Kumba is a major town in southwestern Cameroon known as a commercial hub and cultural crossroads where languages like Cameroonian Pidgin English are widely used.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b9c14c8190b80c3df0cdba2747 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc93a1e4c8190b33478783dcd09d7 |
completed | April 3, 2026, 2:05 p.m. |
| NEDg | Description generation | batch_69cfca3124d88190a6cad0ffb8a67a1a |
completed | April 3, 2026, 2:09 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfcb36b75c8190925eaef1314f4c42 |
completed | April 3, 2026, 2:14 p.m. |
Created at: March 30, 2026, 6:58 p.m.