Triple
T682497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Nations Office at Nairobi |
E13212
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Nairobi |
E6371
|
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: Nairobi | Statement: [United Nations Office at Nairobi, locatedIn, Nairobi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nairobi Context triple: [United Nations Office at Nairobi, locatedIn, Nairobi]
-
A.
Nairobi
chosen
Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
-
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.
Kampala
Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
-
D.
Nakuru
Nakuru is a prominent Kenyan city in the Rift Valley region, known for its proximity to Lake Nakuru National Park and its role as an important agricultural 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.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a06f9ee88190a2d757aacd8e3f5b |
completed | March 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a654d806f08190a8e19f0918f23eb7 |
completed | March 3, 2026, 3:26 a.m. |
Created at: March 1, 2026, 7:36 p.m.