Triple
T33441585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BK |
E856376
|
entity |
| Predicate | cityRankInCountryByPopulation |
P1026
|
FINISHED |
| Object | second largest city in Burkina Faso |
—
|
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: second largest city in Burkina Faso | Statement: [BK, cityRankInCountryByPopulation, second largest city in Burkina Faso]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityRankInCountryByPopulation Context triple: [BK, cityRankInCountryByPopulation, second largest city in Burkina Faso]
-
A.
populationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
B.
hasPopulationRank
chosen
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
C.
countryCapitalRankByPopulation
Indicates the position of a country's capital city in a ranking ordered by population size among all capital cities.
-
D.
hasPopulationRankInRegion
Indicates that an entity has a specific population-based rank or position within a defined geographic region.
-
E.
countryPopulationContext
Indicates the contextual population characteristics or statistics associated with a specific country.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6e4a3c62481909bca99a30c2fa4c1 |
completed | May 3, 2026, 6:01 a.m. |
| PD | Predicate disambiguation | batch_69f6e3da41948190a4cfe866ce184f73 |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:37 a.m.