Triple
T441304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lagos |
E10118
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object | Yaba |
E10118
|
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: Yaba | Statement: [Lagos, hasDistrict, Yaba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yaba Context triple: [Lagos, hasDistrict, Yaba]
-
A.
Ibadan
Ibadan is one of the largest and most populous cities in southwestern Nigeria, historically significant as a major Yoruba cultural and economic center.
-
B.
Ikorodu
Ikorodu is a rapidly growing suburban city and local government area in the northeastern part of Lagos State, Nigeria, known for its residential communities and emerging commercial activity.
-
C.
Kano
Kano is a major commercial and industrial city in northern Nigeria and one of the country’s oldest urban centers.
-
D.
Lagos
Lagos is a historic coastal city in Portugal’s Algarve region, known for its scenic beaches, dramatic cliffs, and well-preserved old town.
-
E.
Lagos
chosen
Lagos is a major coastal megacity in southwestern Nigeria, known as the country’s economic hub and one of Africa’s most populous and vibrant 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef2af84881909635ebbbb3465b1b |
completed | Feb. 28, 2026, 1:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a447fc06288190b74c4047849f615c |
completed | March 1, 2026, 2:06 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.