Triple
T70084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Africa |
E1402
|
entity |
| Predicate | hasNumberOfCountries |
P3809
|
FINISHED |
| Object | 54 |
—
|
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: 54 | Statement: [Africa, hasNumberOfCountries, 54]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCountries Context triple: [Africa, hasNumberOfCountries, 54]
-
A.
hasCountry
Indicates that one entity possesses, is associated with, or is located within a specific country.
-
B.
numberOfRegions
Indicates the total count of distinct regions associated with or contained within a given entity.
-
C.
hasMajorCountry
Indicates that an entity includes, is associated with, or is primarily represented by a particular major country.
-
D.
hasOverseasTerritory
Indicates that one entity possesses or controls a territory located outside its own primary geographic or sovereign domain.
-
E.
hasCountryContext
Indicates that something is associated with, interpreted within, or relevant to a specific country or national context.
- 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_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. |
| PDg | Predicate description generation | batch_69a24fcf5a88819088c5fa4c08476358 |
completed | Feb. 28, 2026, 2:15 a.m. |
Created at: Feb. 28, 2026, 2:03 a.m.