Triple
T70060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Africa |
E1402
|
entity |
| Predicate | coversEarthLandSurface |
P1884
|
FINISHED |
| Object | about 20.4 percent |
—
|
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: about 20.4 percent | Statement: [Africa, coversEarthLandSurface, about 20.4 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coversEarthLandSurface Context triple: [Africa, coversEarthLandSurface, about 20.4 percent]
-
A.
coversFractionOfEarthSurface
chosen
Indicates that something extends over or occupies a specified fraction of the Earth's surface area.
-
B.
hasLandCoverage
Indicates that a specified area or region is covered or occupied by a particular type of land surface or land use.
-
C.
hasLandform
Indicates that one entity possesses, contains, or is characterized by a particular natural landform.
-
D.
hasHydrosphere
Indicates that an entity possesses or is characterized by a surrounding layer or system of water, such as oceans, seas, lakes, or other bodies of liquid water.
-
E.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
- F. None of above.
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_69a24c06b3bc8190aa4ac89026115efc |
completed | Feb. 28, 2026, 1:59 a.m. |
| NER | Named-entity recognition | batch_69a24fd16c248190a6ee4cd96c388772 |
completed | Feb. 28, 2026, 2:15 a.m. |
| PD | Predicate disambiguation | batch_69a24eaa0df88190add55579b2b9fd02 |
completed | Feb. 28, 2026, 2:10 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.