Triple
T6528130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guineans |
E151357
|
entity |
| Predicate | capitalCity |
P204
|
FINISHED |
| Object | Conakry |
E69995
|
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: Conakry | Statement: [Guineans, capitalCity, Conakry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Conakry Context triple: [Guineans, capitalCity, Conakry]
-
A.
Conakry
chosen
Conakry is the capital and largest city of Guinea, serving as its main economic, cultural, and administrative center on the Atlantic coast of West Africa.
-
B.
Navrongo
Navrongo is a town in northern Ghana known as a key administrative and commercial center near the border with Burkina Faso.
-
C.
Bamako
Bamako is the capital and largest city of Mali, serving as a major political, economic, and cultural center in West Africa.
-
D.
Daloa
Daloa is a major inland city in western Côte d'Ivoire known as an important commercial and agricultural center, particularly for cocoa production.
-
E.
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.
- 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_69c687f522748190b3058405553cdabd |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ada9c8408190b1bc327985366be9 |
completed | March 27, 2026, 4:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d52ca9988190addfdae6d7b53a6e |
completed | March 27, 2026, 7:06 p.m. |
Created at: March 27, 2026, 1:46 p.m.