Triple
T46749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Petersburg |
E916
|
entity |
| Predicate | populationApprox |
P1070
|
FINISHED |
| Object | over 5 million |
—
|
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: over 5 million | Statement: [St. Petersburg, populationApprox, over 5 million]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: populationApprox Context triple: [St. Petersburg, populationApprox, over 5 million]
-
A.
population
Indicates the total number of individuals living in or present within a specified area or group.
-
B.
metropolitanAreaPopulationApproximate
chosen
Indicates that the predicate specifies an approximate total population size for a given metropolitan area.
-
C.
populationDensity
Indicates the number of individuals or entities occupying a unit area within a given region.
-
D.
cityPopulationContext
Indicates the contextual relationship between a city and information about its population, such as size, distribution, or demographic characteristics.
-
E.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
- 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_69a2480baefc81909951b14058479aa2 |
completed | Feb. 28, 2026, 1:42 a.m. |
| NER | Named-entity recognition | batch_69a24b1bf2c081908f20e13939b713ff |
completed | Feb. 28, 2026, 1:55 a.m. |
| PD | Predicate disambiguation | batch_69a24abd07508190a83ffba5368c1c79 |
completed | Feb. 28, 2026, 1:54 a.m. |
Created at: Feb. 28, 2026, 1:47 a.m.