Triple
T10450632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Akershus county |
E246411
|
entity |
| Predicate | hadArea |
P175
|
FINISHED |
| Object | approximately 4,917 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: approximately 4,917 square kilometres | Statement: [Akershus county, hadArea, approximately 4,917 square kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadArea Context triple: [Akershus county, hadArea, approximately 4,917 square kilometres]
-
A.
area
chosen
Indicates that one entity has a measured two-dimensional extent or surface size quantified by another entity.
-
B.
hasCivilArea
Indicates that an administrative or political entity encompasses or is associated with a specific civil (local administrative) area.
-
C.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
D.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
E.
coveredArea
Indicates that one entity occupies or extends over a specific spatial region or surface area associated with another entity.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fe0a6a548190a54212912f618e4e |
completed | April 7, 2026, 12:52 p.m. |
| PD | Predicate disambiguation | batch_69d4fb73a5e48190a8df4775bc5da80f |
completed | April 7, 2026, 12:41 p.m. |
Created at: April 6, 2026, 12:17 p.m.