Triple
T1855826
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Togo |
E41698
|
entity |
| Predicate | capitalAndLargestCity |
P8146
|
FINISHED |
| Object | Lomé |
E71688
|
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: Lomé | Statement: [Togo, capitalAndLargestCity, Lomé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lomé Context triple: [Togo, capitalAndLargestCity, Lomé]
-
A.
Lomé
chosen
Lomé is the coastal capital and largest city of Togo, serving as a key economic and cultural hub in West Africa.
-
B.
Cotonou
Cotonou is the largest city and economic hub of Benin, located on the Gulf of Guinea in West Africa.
-
C.
Abidjan
Abidjan is a major economic and cultural hub on the southern coast of Côte d'Ivoire, known for its bustling port, modern skyline, and status as one of the largest cities in West Africa.
-
D.
Yamoussoukro
Yamoussoukro is the political capital of Côte d'Ivoire, known for its grand basilica and role as an administrative center in the French-speaking world.
-
E.
Libreville
Libreville is the largest city and main economic and cultural center of Gabon, located on the country’s Atlantic coast.
- 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_69a8864a83848190a4ec02721306c511 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb07e5ed48190a7b8858e2b355109 |
completed | March 7, 2026, 4:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adfba49f0c8190a6db188f201a6da6 |
completed | March 8, 2026, 10:43 p.m. |
Created at: March 4, 2026, 7:33 p.m.