Triple
T942237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sundbyberg Municipality |
E20330
|
entity |
| Predicate | hasPopulationDensityCharacteristic |
P20594
|
FINISHED |
| Object | one of the most densely populated municipalities in Sweden |
—
|
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: one of the most densely populated municipalities in Sweden | Statement: [Sundbyberg Municipality, hasPopulationDensityCharacteristic, one of the most densely populated municipalities in Sweden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPopulationDensityCharacteristic Context triple: [Sundbyberg Municipality, hasPopulationDensityCharacteristic, one of the most densely populated municipalities in Sweden]
-
A.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
B.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or region.
-
C.
isDenselyPopulated
chosen
Indicates that a place has a high concentration of inhabitants relative to its area.
-
D.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
E.
demographicsCharacteristic
Indicates that one entity serves as a demographic attribute or characteristic (such as age, gender, ethnicity, etc.) that describes or classifies another entity.
- 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_69a493b0270c81909e6c9ce310f6aa55 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3a1a4888190997adf56eb761431 |
completed | March 1, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69a4b29dc8dc8190b9d33f70f8563d61 |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.