Triple
T9034913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nairobi Metropolitan Region |
E216466
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Thika |
E768595
|
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: Thika | Statement: [Nairobi Metropolitan Region, containsTown, Thika]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thika Context triple: [Nairobi Metropolitan Region, containsTown, Thika]
-
A.
Thika
chosen
Thika is a major industrial and commercial town in central Kenya, known for its manufacturing sector and proximity to Nairobi.
-
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.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
D.
Kadoma
Kadoma is a city in central Zimbabwe known for its gold mining and agricultural activities.
-
E.
Kadoma
Kadoma is a city in Osaka Prefecture, Japan, known as a residential and commercial suburb within the Osaka metropolitan area.
- 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_69ca83d10b608190b2b2f8e0a7faaf14 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6abf4af481908d21245332329d99 |
completed | April 1, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d01773694c8190830f9294c3ec4f54 |
completed | April 3, 2026, 7:39 p.m. |
Created at: March 30, 2026, 7:08 p.m.