Triple
T11075444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aga Khan University |
E261853
|
entity |
| Predicate | campus |
P269
|
FINISHED |
| Object | Kampala |
E40695
|
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: Kampala | Statement: [Aga Khan University, campus, Kampala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kampala Context triple: [Aga Khan University, campus, Kampala]
-
A.
Kampala
chosen
Kampala is the capital and largest city of Uganda, serving as the country’s political, economic, and cultural center.
-
B.
Entebbe
Entebbe is a town in central Uganda on a peninsula into Lake Victoria, known for its international airport and the site of the 1976 hostage-rescue operation.
-
C.
Dodoma
Dodoma is the political and administrative capital city of Tanzania, located in the country’s central region.
-
D.
Lipa City
Lipa City is a highly urbanized city in Batangas, Philippines, known as a commercial, educational, and religious center in the Calabarzon region.
-
E.
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.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7994efb608190a81bc8c4d16ddbd0 |
completed | April 9, 2026, 12:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e3e77a78288190aa76912e0fa821b5 |
completed | April 18, 2026, 8:20 p.m. |
Created at: April 8, 2026, 9:26 p.m.