Triple
T2118073
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kisumu |
E43852
|
entity |
| Predicate | hasPort |
P35
|
FINISHED |
| Object | Kisumu Port |
E43852
|
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: Kisumu Port | Statement: [Kisumu, hasPort, Kisumu Port]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kisumu Port Context triple: [Kisumu, hasPort, Kisumu Port]
-
A.
Kisumu
chosen
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
-
B.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
C.
Speke Airport
Speke Airport was the original name of the international airport serving Liverpool, England, now known as Liverpool John Lennon Airport.
-
D.
Embakasi
Embakasi is a residential and industrial area in Nairobi, Kenya, known for hosting key infrastructure and serving as a major gateway corridor to the city.
-
E.
Gombe
Gombe is a region in western Tanzania best known for its national park where pioneering primatologist Jane Goodall conducted her landmark chimpanzee research.
- 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_69a88717cfe48190b7ecdd68c824848a |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abbb3117c081908c5e748a869d1f9f |
completed | March 7, 2026, 5:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae307b08148190aa201ac038ce9944 |
completed | March 9, 2026, 2:29 a.m. |
Created at: March 4, 2026, 7:44 p.m.