Triple
T16489795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Capital of Culture programme |
E400538
|
entity |
| Predicate | typicalNumberOfCitiesPerYear |
P27365
|
FINISHED |
| Object | one or more |
—
|
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 or more | Statement: [European Capital of Culture programme, typicalNumberOfCitiesPerYear, one or more]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalNumberOfCitiesPerYear Context triple: [European Capital of Culture programme, typicalNumberOfCitiesPerYear, one or more]
-
A.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
numberOfHostCities
Indicates the count of distinct cities that have hosted or will host a particular event or activity.
-
C.
numberOfParticipatingCities
chosen
Indicates the total count of cities that take part in a specified event, program, or activity.
-
D.
typicalNumberOfStopsPerSeason
Indicates the usual or average count of stops that occur in a single season.
-
E.
gatheredCities
Indicates that one entity collected or assembled multiple cities together, typically into a group, list, or shared context.
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32e2e3bf88190ba6eac85a79e5ac8 |
completed | April 18, 2026, 7:09 a.m. |
| PD | Predicate disambiguation | batch_69e296902d6c8190884ddb612b8c5b36 |
completed | April 17, 2026, 8:22 p.m. |
Created at: April 10, 2026, 5:13 a.m.