Triple
T14852292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kazaure |
E349259
|
entity |
| Predicate | hasNearbyUrbanCenter |
P36605
|
FINISHED |
| Object | Dutse |
E359210
|
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: Dutse | Statement: [Kazaure, hasNearbyUrbanCenter, Dutse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dutse Context triple: [Kazaure, hasNearbyUrbanCenter, Dutse]
-
A.
Dutse
chosen
Dutse is a city in northern Nigeria that serves as the administrative and economic center of Jigawa State.
-
B.
Dutse
Dutse is a locality within Nigeria’s Federal Capital Territory, situated in the Bwari Area Council on the outskirts of Abuja.
-
C.
Kaltungo
Kaltungo is a town and administrative center in northeastern Nigeria known for its role as one of the local government areas within Gombe State.
-
D.
Kano
Kano is a long-running Mortal Kombat villain known as a ruthless mercenary and leader of the Black Dragon crime syndicate, often depicted with a cybernetic eye and expertise in knives and dirty fighting tactics.
-
E.
Kano
Kano is a major commercial and industrial city in northern Nigeria and one of the country’s oldest urban centers.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded441e70881909bbf62b66d932aff |
completed | April 14, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b4ce76881909bf4a967da9357ae |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:54 a.m.