Triple
T12100593
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Itsekiri language |
E288179
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Ondo State |
E60520
|
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: Ondo State | Statement: [Itsekiri language, region, Ondo State]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ondo State Context triple: [Itsekiri language, region, Ondo State]
-
A.
Ondo State
chosen
Ondo State is a coastal state in southwestern Nigeria known for its oil-producing areas, diverse ethnic communities, and significant role within the Niger Delta region.
-
B.
Ogun State
Ogun State is a southwestern Nigerian state known as a key Yoruba cultural heartland and an important industrial and educational hub.
-
C.
Osun State
Osun State is a southwestern Nigerian state that is a cultural and historical heartland of the Yoruba people, known for its traditional festivals and religious heritage.
-
D.
Kwara State
Kwara State is a north-central Nigerian state with a significant Yoruba population and a cultural blend of northern and southwestern Nigerian influences.
-
E.
Ekiti State
Ekiti State is a landlocked, predominantly Yoruba-speaking state in southwestern Nigeria known for its hilly terrain and strong emphasis on education.
- 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_69d6ab4a5c448190a110d1273314b21a |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9155576b48190849f0c3e079a935f |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdfb6b97888190923ce7a6e59ff752 |
completed | May 8, 2026, 3:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.