Triple
T659902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Ville Radieuse |
E11729
|
entity |
| Predicate | proposedPopulationDensity |
P797
|
FINISHED |
| Object | very 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: very high | Statement: [La Ville Radieuse, proposedPopulationDensity, very high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proposedPopulationDensity Context triple: [La Ville Radieuse, proposedPopulationDensity, very high]
-
A.
hasPopulationDensity
Indicates the number of individuals (e.g., people, organisms) per unit area associated with a given entity or region.
-
B.
populationDensity
chosen
Indicates the number of individuals or entities occupying a unit area within a given region.
-
C.
hasPopulationCenterDensity
Indicates the density of population centers within a given area or region.
-
D.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
E.
metropolitanAreaPopulationApproximate
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
- 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_69a4932862a0819098be659c814e4981 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d1406ec8190abf546549264c85d |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:36 p.m.