Triple
T21199189
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kilifi County |
E522405
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Mtwapa
Mtwapa is a rapidly growing coastal town in Kenya known for its vibrant nightlife, tourism, and proximity to Mombasa.
|
E1471911
|
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: Mtwapa | Statement: [Kilifi County, hasSettlement, Mtwapa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mtwapa Context triple: [Kilifi County, hasSettlement, Mtwapa]
-
A.
Lipa City
Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
-
B.
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.
-
C.
Thika
Thika is a major industrial and commercial town in central Kenya, known for its manufacturing sector and proximity to Nairobi.
-
D.
Nakur
Nakur is a small town in the Saharanpur district of Uttar Pradesh, India, known for its local markets and role as a regional administrative and commercial center.
-
E.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
- 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: Mtwapa Triple: [Kilifi County, hasSettlement, Mtwapa]
Generated description
Mtwapa is a rapidly growing coastal town in Kenya known for its vibrant nightlife, tourism, and proximity to Mombasa.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mtwapa Target entity description: Mtwapa is a rapidly growing coastal town in Kenya known for its vibrant nightlife, tourism, and proximity to Mombasa.
-
A.
Lipa City
Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
-
B.
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.
-
C.
Thika
Thika is a major industrial and commercial town in central Kenya, known for its manufacturing sector and proximity to Nairobi.
-
D.
Nakur
Nakur is a small town in the Saharanpur district of Uttar Pradesh, India, known for its local markets and role as a regional administrative and commercial center.
-
E.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
- 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_69e0b51061388190aa03f19700d3ef04 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7342fe3a08190b7ed2cadf60091a8 |
completed | April 21, 2026, 8:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a097ecd1d1c819081a3a301701a11ae |
completed | May 17, 2026, 8:39 a.m. |
| NEDg | Description generation | batch_6a09807278ac8190ae2835ce6d79a9cc |
completed | May 17, 2026, 8:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09810158948190a9504d50c964efd9 |
completed | May 17, 2026, 8:49 a.m. |
Created at: April 16, 2026, 3:17 p.m.