Triple
T226970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Union |
E4332
|
entity |
| Predicate | hasAreaApprox |
P6061
|
FINISHED |
| Object | 4.2 million square kilometres |
—
|
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: 4.2 million square kilometres | Statement: [European Union, hasAreaApprox, 4.2 million square kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAreaApprox Context triple: [European Union, hasAreaApprox, 4.2 million square kilometres]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
C.
approximateMass
Indicates that one entity has a mass value that is an estimate or close approximation of the mass of another entity.
-
D.
approximateSize
chosen
Indicates that one entity has a size that is roughly or approximately equal to the size of another entity.
-
E.
approximateDiameter
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
- 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_69a257363ffc81909757bde7ab3404da |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25d10ac248190a98dedabf5358668 |
completed | Feb. 28, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69a25b5877588190af694d060377f027 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.