Triple
T296755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woodbridge, Ontario |
E6107
|
entity |
| Predicate | dominantLandUse |
P5744
|
FINISHED |
| Object | low-density residential |
—
|
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: low-density residential | Statement: [Woodbridge, Ontario, dominantLandUse, low-density residential]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dominantLandUse Context triple: [Woodbridge, Ontario, dominantLandUse, low-density residential]
-
A.
dominantRegion
Indicates that one region exerts primary control, influence, or prominence over another region within a given context.
-
B.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
C.
dominantOn
Indicates that one entity exerts control, authority, or prevailing influence over another in a given context.
-
D.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
E.
urbanDevelopmentType
chosen
Indicates the specific category or nature of urban development associated with or applied to an entity (e.g., residential, commercial, mixed-use).
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea4778cc8190be7b648a82542891 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e937af888190a0960708f09ae033 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.