Triple
T1154899
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghana |
E23760
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Accra |
E68377
|
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: Accra | Statement: [Ghana, largestCity, Accra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Accra Context triple: [Ghana, largestCity, Accra]
-
A.
Accra
chosen
Accra is the capital and largest city of Ghana, known as a major economic, political, and cultural hub in West Africa.
-
B.
Freetown
Freetown is the capital and largest city of Sierra Leone, known as a historic port and former center for resettled freed slaves 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.
Bamako
Bamako is the capital and largest city of Mali, serving as a major political, economic, and cultural center in West Africa.
-
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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc8fbb548190865b1bf019f2bde4 |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac667a61248190b71033daadef58e3 |
completed | March 7, 2026, 5:55 p.m. |
Created at: March 1, 2026, 7:44 p.m.