Triple
T1940584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Gambia |
E41541
|
entity |
| Predicate | areaRankingInAfrica |
P34182
|
FINISHED |
| Object | smallest country on mainland Africa |
—
|
LITERAL 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: smallest country on mainland Africa | Statement: [The Gambia, areaRankingInAfrica, smallest country on mainland Africa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaRankingInAfrica Context triple: [The Gambia, areaRankingInAfrica, smallest country on mainland Africa]
-
A.
economyRankInAfricaByGDP
Indicates the relative position of an African country's economy when ordered by the size of its Gross Domestic Product (GDP) compared to other African countries.
-
B.
continentRankByArea
Indicates the relative position of a continent in an ordered list based on its total land area.
-
C.
rankInAfricaByLength
Indicates the position of something in an ordered list of African entities sorted by their length (e.g., size, distance, or extent).
-
D.
rankInWorldByArea
Indicates the position of an entity in a global ordering based on its total area size.
-
E.
areaRankingInEurope
Indicates the position of an entity in a size-based ranking of areas within Europe.
- F. None of above. chosen
Provenance (4 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb32d35508190bf1c487dffbecaf0 |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abaff25a588190bb4cbc8df9fc6d64 |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb32a8d548190a231c7c2ce276a5e |
completed | March 7, 2026, 5:10 a.m. |
Created at: March 4, 2026, 7:36 p.m.