Triple
T30524764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vavilova Street |
E776801
|
entity |
| Predicate | hasUrbanZoneType |
P105265
|
FINISHED |
| Object | residential area |
—
|
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: residential area | Statement: [Vavilova Street, hasUrbanZoneType, residential area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasUrbanZoneType Context triple: [Vavilova Street, hasUrbanZoneType, residential area]
-
A.
isUrbanZoneFor
Indicates that a given area functions as an urban zone designated for a particular entity or purpose.
-
B.
isUrbanZone
Indicates that a given area or region is classified as an urban zone, typically characterized by higher population density and developed infrastructure.
-
C.
hasUrbanDistrictFunction
Indicates that an entity serves the administrative or functional role of an urban district within a larger territorial or governance structure.
-
D.
hasUrbanPlanningZone
Indicates that an area or property is designated as belonging to a specific urban planning zone within a planning or zoning scheme.
-
E.
isUrbanAreaOfType
chosen
Indicates that a given area is classified as belonging to a specific type or category of urban area (e.g., city, town, suburb).
- 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_69f2249b23c4819087fa85496d92f43f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6953bafb88190a860e9c68a3dd4b2 |
completed | May 3, 2026, 12:22 a.m. |
| PD | Predicate disambiguation | batch_69f690ed5d008190831cf8e44cce28af |
completed | May 3, 2026, 12:03 a.m. |
Created at: April 29, 2026, 8:17 p.m.