Triple
T3912007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayero University Kano |
E87343
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Kano |
E13198
|
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: Kano | Statement: [Bayero University Kano, locatedIn, Kano]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kano Context triple: [Bayero University Kano, locatedIn, Kano]
-
A.
Kano
chosen
Kano is a major commercial and industrial city in northern Nigeria and one of the country’s oldest urban centers.
-
B.
Sokoto
Sokoto is a historic city in northwestern Nigeria that served as the capital of the Sokoto Caliphate and remains an important cultural and Islamic scholarly center.
-
C.
Dutse
Dutse is a city in northern Nigeria that serves as the administrative and economic center of Jigawa State.
-
D.
Zaria
Zaria is a historic city in northern Nigeria known as an important center of Hausa culture, Islamic scholarship, and trade.
-
E.
Kaduna
Kaduna is a major industrial and political center in northern Nigeria, known for its diverse population and role as the capital of Kaduna State.
- 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aeed35e2d081908b5d87c7630e7ffc |
completed | March 9, 2026, 3:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b51cb454c48190bf47d080f6cc24f0 |
completed | March 14, 2026, 8:30 a.m. |
Created at: March 9, 2026, 3:22 p.m.