Triple
T35297470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maaninka |
E1019410
|
entity |
| Predicate | populationDensityType |
P63445
|
FINISHED |
| Object | sparsely populated |
—
|
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: sparsely populated | Statement: [Maaninka, populationDensityType, sparsely populated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationDensityType Context triple: [Maaninka, populationDensityType, sparsely populated]
-
A.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
B.
hasPopulationDensityType
chosen
Indicates the classification of an area based on how densely populated it is (e.g., urban, suburban, rural).
-
C.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
D.
populationDensityComparedToRestOfCountry
Indicates how the population density of a specific area compares to the average population density of the rest of the country.
-
E.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or 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_69f76de7eedc8190a3bdc64ebbc05b42 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fd49f6dbac81909744373a357b7982 |
completed | May 8, 2026, 2:27 a.m. |
| PD | Predicate disambiguation | batch_69fd48ed68f481908374183c66a6b055 |
completed | May 8, 2026, 2:22 a.m. |
Created at: May 3, 2026, 4:03 p.m.