Triple
T11382270
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dubrovnik Airport |
E269624
|
entity |
| Predicate | countryRankByPassengerTraffic |
P19832
|
FINISHED |
| Object | one of the busiest airports in Croatia |
—
|
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 of the busiest airports in Croatia | Statement: [Dubrovnik Airport, countryRankByPassengerTraffic, one of the busiest airports in Croatia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryRankByPassengerTraffic Context triple: [Dubrovnik Airport, countryRankByPassengerTraffic, one of the busiest airports in Croatia]
-
A.
passengerTrafficRankingWorld
Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
-
B.
peakPassengerTrafficRank
Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
-
C.
countryWithMostPassengers
Indicates the country associated with the highest number of passengers in a given context or dataset.
-
D.
airportRank
chosen
Indicates the relative position or level assigned to an airport within a ranking or ordered list.
-
E.
passengerTrafficRankUS
Indicates the relative ranking of a location or facility within the United States based on the volume of passenger traffic it handles.
- 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_69d6aacca1048190b39dbbc2174616fa |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d800160a1c81909d115bf89fe54a49 |
completed | April 9, 2026, 7:37 p.m. |
| PD | Predicate disambiguation | batch_69d7e70b228c8190b87f5101fd683788 |
completed | April 9, 2026, 5:51 p.m. |
Created at: April 8, 2026, 9:34 p.m.