Triple
T13098398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bassey Otu |
E310650
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Bassey Otu |
E310650
|
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: Bassey Otu | Statement: [Bassey Otu, name, Bassey Otu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bassey Otu Context triple: [Bassey Otu, name, Bassey Otu]
-
A.
Bassey Otu
chosen
Bassey Otu is a Nigerian politician serving as the governor of Cross River State.
-
B.
Obong Victor Attah
Obong Victor Attah is a Nigerian architect and politician who served as the governor of Akwa Ibom State from 1999 to 2007.
-
C.
Samuel Aba
Samuel Aba was an 11th-century King of Hungary who came to power after deposing Peter Orseolo and is known for his short, turbulent reign and eventual overthrow.
-
D.
Kofi Adu
Kofi Adu, popularly known as Agya Koo, is a renowned Ghanaian actor and comedian celebrated for his influential roles in Kumawood films.
-
E.
DeObia Oparei
DeObia Oparei is a British actor and playwright known for his roles in film and television, including appearances in projects like Game of Thrones and various major studio movies.
- 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_69d806a733548190989cfd4ce981ca33 |
completed | April 9, 2026, 8:05 p.m. |
| NER | Named-entity recognition | batch_69d981500d34819097037b3c3c33627b |
completed | April 10, 2026, 11:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d619b82c819093d0d98db88eb9ae |
completed | May 3, 2026, 4:59 a.m. |
Created at: April 9, 2026, 9:04 p.m.