Triple
T21175434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kwale County |
E521798
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object |
Kinango
Kinango is a small inland town in Kenya’s coastal region that serves as an administrative and commercial center within Kwale County.
|
E1470688
|
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: Kinango | Statement: [Kwale County, hasMajorTown, Kinango]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kinango Context triple: [Kwale County, hasMajorTown, Kinango]
-
A.
Kasangati
Kasangati is a town in central Uganda that serves as a growing commercial and residential hub within the Greater Kampala metropolitan area.
-
B.
Kesinga
Kesinga is a prominent town in the Kalahandi district of Odisha, India, known as an important local commercial and transportation hub.
-
C.
Karanga
Karanga is a major dialect of the Shona language spoken primarily in southern Zimbabwe, known for its distinct phonological and lexical features.
-
D.
Kianga
Kianga is a rural locality within Queensland’s Banana Shire, known for its agricultural and mining activities.
-
E.
Kitengela
Kitengela is a rapidly growing commuter town in Kenya known for its residential estates, industrial development, and proximity to Nairobi.
- 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: Kinango Triple: [Kwale County, hasMajorTown, Kinango]
Generated description
Kinango is a small inland town in Kenya’s coastal region that serves as an administrative and commercial center within Kwale County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kinango Target entity description: Kinango is a small inland town in Kenya’s coastal region that serves as an administrative and commercial center within Kwale County.
-
A.
Kasangati
Kasangati is a town in central Uganda that serves as a growing commercial and residential hub within the Greater Kampala metropolitan area.
-
B.
Kesinga
Kesinga is a prominent town in the Kalahandi district of Odisha, India, known as an important local commercial and transportation hub.
-
C.
Karanga
Karanga is a major dialect of the Shona language spoken primarily in southern Zimbabwe, known for its distinct phonological and lexical features.
-
D.
Kianga
Kianga is a rural locality within Queensland’s Banana Shire, known for its agricultural and mining activities.
-
E.
Kitengela
Kitengela is a rapidly growing commuter town in Kenya known for its residential estates, industrial development, and proximity to Nairobi.
- 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_69e0b50e30748190b186824a206d39b9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7271597288190b04baff9ca8d866c |
completed | April 21, 2026, 7:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09757772b88190b7eab700ab116596 |
completed | May 17, 2026, 7:59 a.m. |
| NEDg | Description generation | batch_6a09775849588190a7d28b0bb6912a01 |
completed | May 17, 2026, 8:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a097878a6248190a3573b84aaa29827 |
completed | May 17, 2026, 8:12 a.m. |
Created at: April 16, 2026, 3 p.m.