Triple
T563911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southwestern New South Wales |
E13511
|
entity |
| Predicate | urbanisationLevel |
P9969
|
FINISHED |
| Object | largely rural |
—
|
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: largely rural | Statement: [Southwestern New South Wales, urbanisationLevel, largely rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: urbanisationLevel Context triple: [Southwestern New South Wales, urbanisationLevel, largely rural]
-
A.
urbanizationLevel
chosen
Indicates the degree to which an area or population is characterized by urban development, infrastructure, and density of human settlement.
-
B.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
C.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
D.
containsUrbanArea
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
E.
largestUrbanConcentrationIn
Indicates that an entity represents the biggest or most populous urban area located within a specified geographic region.
- 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_69a4933edcf08190b35ecfd6014caee6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49a712bc48190ba298b3c76ab11cc |
completed | March 1, 2026, 7:58 p.m. |
| PD | Predicate disambiguation | batch_69a494c044648190a98589ab18935216 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:32 p.m.