Triple
T91855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luxembourg |
E1844
|
entity |
| Predicate | areaSquareKilometres |
P157
|
FINISHED |
| Object | about 2586 |
—
|
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 2586 | Statement: [Luxembourg, areaSquareKilometres, about 2586]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaSquareKilometres Context triple: [Luxembourg, areaSquareKilometres, about 2586]
-
A.
landArea
chosen
Indicates the total surface area of a piece of land associated with an entity, typically measured in standardized units (e.g., square meters, hectares).
-
B.
areaWater
Indicates the relationship between a geographic entity and the total area of its surface that is covered by water.
-
C.
area
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
D.
drainageBasinArea
Indicates the total surface area of land from which precipitation and runoff drain into a particular water body or watershed.
-
E.
hasLargestCountryByArea
Indicates that, among a set of compared entities, the subject is associated with the country that has the greatest land area.
- 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_69a24d1a97dc819094e6c021fe9b05a7 |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a2512ef600819084d3c627f0d534f4 |
completed | Feb. 28, 2026, 2:21 a.m. |
| PD | Predicate disambiguation | batch_69a24eb9a5ac8190b1d1300e8c4e3606 |
completed | Feb. 28, 2026, 2:11 a.m. |
Created at: Feb. 28, 2026, 2:07 a.m.