Triple
T14352348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dantokpa Market |
E355885
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Cotonou |
E71791
|
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: Cotonou | Statement: [Dantokpa Market, locatedIn, Cotonou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cotonou Context triple: [Dantokpa Market, locatedIn, Cotonou]
-
A.
Cotonou
chosen
Cotonou is the largest city and economic hub of Benin, located on the Gulf of Guinea in West Africa.
-
B.
Abidji
Abidji is a Kwa language of the Central Tano subgroup spoken by the Abidji people in southern Côte d’Ivoire.
-
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.
Parakou
Parakou is a major city in central Benin that serves as an important commercial and transportation hub for the surrounding region.
-
E.
Lomé
Lomé is the coastal capital and largest city of Togo, serving as a key economic and cultural hub in West Africa.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8f4ff1e48190bd9419d70098cede |
completed | April 14, 2026, 7:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe64e68118819082448393cc141d96 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:14 a.m.