Triple
T1036215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niger |
E22368
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Niamey |
E72364
|
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: Niamey | Statement: [Niger, largestCity, Niamey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niamey Context triple: [Niger, largestCity, Niamey]
-
A.
Niamey
chosen
Niamey is the capital and largest city of Niger, situated along the Niger River and serving as the country’s political, economic, and cultural center.
-
B.
Bamako
Bamako is the capital and largest city of Mali, serving as a major political, economic, and cultural center in West Africa.
-
C.
Ouagadougou
Ouagadougou is the capital and largest city of Burkina Faso, serving as its political, economic, and cultural center in the Sahel region.
-
D.
N'Djamena
N'Djamena is the largest city and political, economic, and cultural center of Chad, located in the southwestern part of the country near the border with Cameroon.
-
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_69a493d848848190aed4011b34b2e8d3 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b816272c8190a12e470c4d4ebcf9 |
completed | March 1, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac4c15bb8481909ba68f5807581b18 |
completed | March 7, 2026, 4:02 p.m. |
Created at: March 1, 2026, 7:41 p.m.