Triple
T949532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stanislaus County |
E20487
|
entity |
| Predicate | hasSuburbanGrowth |
P22501
|
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: [Stanislaus County, hasSuburbanGrowth, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSuburbanGrowth Context triple: [Stanislaus County, hasSuburbanGrowth, yes]
-
A.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
B.
hasSuburbanSection
Indicates that a larger route, line, or area includes a portion that passes through or serves a suburban region.
-
C.
hasSuburbanCharacter
Indicates that something possesses qualities or features typically associated with suburban areas, such as lower density, residential focus, and car-oriented development.
-
D.
hasMajorSuburb
Indicates that a larger urban area includes or is associated with a specific major suburb within its boundaries or sphere.
-
E.
isSuburbanCounty
Indicates that a county is classified as suburban, typically lying outside a central city and characterized by intermediate population density and development.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3c191ac819099ebf3cb32f096d8 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29f05f481908814bd11f235e9d0 |
completed | March 1, 2026, 9:41 p.m. |
| PDg | Predicate description generation | batch_69a4b385176081909e3e8c3f647c1fd4 |
completed | March 1, 2026, 9:45 p.m. |
Created at: March 1, 2026, 7:40 p.m.