Triple
T34864254
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Poleshuks |
E1004968
|
entity |
| Predicate | traditionallyRural |
P140859
|
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: [Poleshuks, traditionallyRural, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: traditionallyRural Context triple: [Poleshuks, traditionallyRural, true]
-
A.
semiRuralCharacter
Indicates that a place or area has characteristics intermediate between rural and urban, combining elements of both environments.
-
B.
traditionalLifestyle
Indicates that an entity follows or maintains long-established customs, practices, and ways of living, typically in contrast to modern or industrialized lifestyles.
-
C.
traditionalLifestyleRegion
chosen
Indicates that a region is characterized by or associated with a traditional lifestyle or way of living.
-
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.
traditionalCountry
Indicates that a country is characterized by long-established customs, cultural practices, and social norms that have been preserved over time.
- 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_69f76dbb678081909a247b9b5e1a73ac |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff41645c548190b7cb4e53079b93ef |
completed | May 9, 2026, 2:15 p.m. |
| PD | Predicate disambiguation | batch_69ff410aa33c8190869ba769ac2a93ce |
completed | May 9, 2026, 2:13 p.m. |
Created at: May 3, 2026, 4 p.m.