Triple
T6435847
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Cameroon |
E129891
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Douala |
E132327
|
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: Douala | Statement: [Mount Cameroon, nearCity, Douala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Douala Context triple: [Mount Cameroon, nearCity, Douala]
-
A.
Douala
chosen
Douala is the economic capital and main port city of Cameroon, located on the Wouri River along the Atlantic coast.
-
B.
Yaoundé
Yaoundé is the political and administrative center of Cameroon, known for its hilly terrain and role as a major cultural and economic hub in Central Africa.
-
C.
Libreville
Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
-
D.
Ouaga
Ouaga is the commonly used short name for Ouagadougou, the capital and largest city of Burkina Faso.
-
E.
Pointe-Noire
Pointe-Noire is a major port city on the Atlantic coast of the Republic of the Congo and one of the country’s principal economic and industrial 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_69c0084caac48190a7bc2ad8ba44536f |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c069622eb881908b40fc8079d312d6 |
completed | March 22, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6eec623c081908352580b64218ec4 |
completed | March 27, 2026, 8:55 p.m. |
Created at: March 22, 2026, 4:45 p.m.