Triple
T14361632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Togolese presidency |
E356116
|
entity |
| Predicate | seat |
P75
|
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: [Togolese presidency, seat, Lomé]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lomé Context triple: [Togolese presidency, seat, 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.
Abomey-Calavi
Abomey-Calavi is a major city in southern Benin, functioning as a rapidly growing suburban and academic hub near the economic capital Cotonou.
-
E.
Abidji
Abidji is a Kwa language of the Central Tano subgroup spoken by the Abidji people in southern Côte d’Ivoire.
- 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_69de8fabec088190bd8128371b29e958 |
completed | April 14, 2026, 7:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69feadfaddc88190bb1196ace0bfd4ff |
completed | May 9, 2026, 3:46 a.m. |
Created at: April 10, 2026, 1:15 a.m.