Triple
T28740891
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Urzhumsky District |
E731235
|
entity |
| Predicate | hasRuralTerritories |
P54976
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Urzhumsky District, hasRuralTerritories, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRuralTerritories Context triple: [Urzhumsky District, hasRuralTerritories, yes]
-
A.
hasRuralArea
Indicates that an entity includes, is associated with, or contains a countryside or sparsely populated geographic area.
-
B.
hasRuralHinterland
Indicates that a place or urban area is associated with and served by a surrounding rural region that supports it economically, socially, or functionally.
-
C.
hasRuralCommunes
Indicates that an entity possesses, includes, or is associated with one or more rural communes.
-
D.
hasRuralSection
Indicates that an entity includes, contains, or is associated with a portion or segment located in a rural area.
-
E.
hasRuralLocality
chosen
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.
- 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_69f043ecb5c081909ec9da1172d68ece |
completed | April 28, 2026, 5:21 a.m. |
| NER | Named-entity recognition | batch_69f676f968d08190a4adba0439b438c9 |
completed | May 2, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 28, 2026, 6:02 a.m.