Triple
T8657404
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Parkwood |
E205454
|
entity |
| Predicate | housingDensity |
P70081
|
FINISHED |
| Object | low to medium density |
—
|
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 to medium density | Statement: [Parkwood, housingDensity, low to medium density]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: housingDensity Context triple: [Parkwood, housingDensity, low to medium density]
-
A.
hasHousingDensity
chosen
Indicates the relationship between an area and the concentration of housing units within that area, typically measured as units per unit of land.
-
B.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
C.
populationConcentration
Indicates the degree to which a population is densely gathered or distributed within a specific area or region.
-
D.
hasPopulationDensityType
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
-
E.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given 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_69ca8350897c819086cde7596fbe5fe7 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc486d576081908ad28749c7971432 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc45619460819091e83ffdec99c865 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.