Triple
T7001197
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Internet in Poland |
E162339
|
entity |
| Predicate | ruralCoverage |
P73513
|
FINISHED |
| Object | lower than urban |
—
|
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: lower than urban | Statement: [Internet in Poland, ruralCoverage, lower than urban]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ruralCoverage Context triple: [Internet in Poland, ruralCoverage, lower than urban]
-
A.
hasRuralServiceTown
Indicates that a rural area or region is associated with, and typically receives services from, a specific town that functions as its service center.
-
B.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
C.
isInRuralAreaOf
Indicates that one entity is located within the rural area or countryside region associated with another entity.
-
D.
isPredominantlyRural
Indicates that a place or region is characterized mainly by rural features, such as low population density and extensive non-urban land use.
-
E.
isRural
Indicates that something is located in, characteristic of, or associated with a countryside or non-urban area.
- F. None of above. chosen
Provenance (4 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc0f8830819091f4356296234713 |
completed | March 27, 2026, 7:35 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c67c94819084fdcf0398606027 |
completed | March 27, 2026, 7:17 p.m. |
| PDg | Predicate description generation | batch_69c6d8c575f081908b43d95d1d99b1a4 |
completed | March 27, 2026, 7:21 p.m. |
Created at: March 27, 2026, 2:33 p.m.