Triple
T10164325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wamba |
E233968
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Wamba |
E233968
|
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: Wamba | Statement: [Wamba, givenName, Wamba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wamba Context triple: [Wamba, givenName, Wamba]
-
A.
Wamba
chosen
Wamba was a 7th-century king of the Visigoths in Hispania, known for his military campaigns and efforts to strengthen royal authority.
-
B.
Wamba
Wamba is a town and administrative local government area in Nasarawa State, central Nigeria, known for its diverse ethnic communities and agricultural activities.
-
C.
Kenzi
Kenzi is a Nubian language spoken in southern Egypt, closely related to Nobiin and part of the broader Nubian language family along the Nile.
-
D.
Tembo
Tembo are a Bantu-speaking ethnic group primarily inhabiting the eastern region of the Democratic Republic of the Congo, known for their agrarian lifestyle and rich cultural traditions.
-
E.
Dongo
Dongo is a small town on the northwestern shore of Lake Como in Lombardy, Italy, known for its role in the capture of Benito Mussolini at the end of World War II.
- 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_69ca848e80748190b91d1e04d35512c7 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cdec6b96dc8190ae37d0d28e4c393b |
completed | April 2, 2026, 4:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d300d672fc8190ad5b937d02a737fd |
completed | April 6, 2026, 12:39 a.m. |
Created at: March 30, 2026, 9:09 p.m.