Triple
T28769769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kronprinzstraße (Stuttgart) |
E726377
|
entity |
| Predicate | hasCommercialDensity |
P81830
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Kronprinzstraße (Stuttgart), hasCommercialDensity, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCommercialDensity Context triple: [Kronprinzstraße (Stuttgart), hasCommercialDensity, high]
-
A.
hasHousingDensity
Indicates the relationship between an area and the concentration of housing units within that area, typically measured as units per unit of land.
-
B.
hasHighDensityOf
chosen
Indicates that one entity contains or exhibits a large concentration or amount of another entity within a given area, volume, or context.
-
C.
hasCommercialInfrastructure
Indicates that an entity possesses or is equipped with facilities, systems, or structures that support commercial or business activities.
-
D.
hasCentralDensity
Indicates that an entity possesses a specified value for its density at the central or core region.
-
E.
hasCommercialCenterType
Indicates that an entity has or is associated with a specific type or category of commercial center (e.g., mall, shopping district, business park).
- 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_69f03198be14819098fa74e48b3749bf |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69ffecdcbac4819093b725a7dbe0e61b |
completed | May 10, 2026, 2:26 a.m. |
| PD | Predicate disambiguation | batch_69ffec3633288190adbbd84e277708dc |
completed | May 10, 2026, 2:23 a.m. |
Created at: April 28, 2026, 6:15 a.m.