Triple
T15845772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Löwenberger Land |
E384208
|
entity |
| Predicate | isPartOfRuralArea |
P45725
|
FINISHED |
| Object | Brandenburg countryside |
—
|
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: Brandenburg countryside | Statement: [Löwenberger Land, isPartOfRuralArea, Brandenburg countryside]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isPartOfRuralArea Context triple: [Löwenberger Land, isPartOfRuralArea, Brandenburg countryside]
-
A.
isInRuralAreaOf
chosen
Indicates that one entity is located within the rural area or countryside region associated with another entity.
-
B.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
C.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
D.
hasRuralLocality
Indicates that an entity possesses, includes, or is associated with a rural locality (such as a village, hamlet, or countryside settlement) within its scope or jurisdiction.
-
E.
isRuralRegion
Indicates that a region is classified as rural, typically characterized by low population density and limited urban development.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14ca636048190bc89cd8b4efa89e9 |
completed | April 16, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69e005434ed88190baf11c169da3cf29 |
completed | April 15, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:50 a.m.