Triple
T25167818
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dąbrowa County |
E630228
|
entity |
| Predicate | hasPredominantlyRuralCharacter |
P47416
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Dąbrowa County, hasPredominantlyRuralCharacter, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPredominantlyRuralCharacter Context triple: [Dąbrowa County, hasPredominantlyRuralCharacter, true]
-
A.
isPredominantlyRural
chosen
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
B.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
C.
hasRuralFocus
Indicates that the subject is oriented toward, concerned with, or primarily serving rural areas or rural-related issues.
-
D.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
-
E.
hasRuralLifestyle
Indicates that an entity lives in or regularly engages in a way of life characteristic of rural areas, such as farming, low population density, and countryside-oriented activities.
- 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_69e75a87c9b88190ab60731902a99750 |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f68805b4848190b75da14996d52a38 |
completed | May 2, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 21, 2026, 12:18 p.m.