Triple
T14867243
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Budapest Ferenc Liszt International Airport |
E349646
|
entity |
| Predicate | hasNonSchengenTraffic |
P33077
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Budapest Ferenc Liszt International Airport, hasNonSchengenTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNonSchengenTraffic Context triple: [Budapest Ferenc Liszt International Airport, hasNonSchengenTraffic, yes]
-
A.
supportsNonSchengenFlights
chosen
Indicates that the subject facility or service is capable of handling or accommodating flights that operate outside the Schengen Area.
-
B.
hasSchengenArea
Indicates that a place, country, or region is part of, or included within, the Schengen Area for border-free movement.
-
C.
isSchengenExternalBorder
Indicates that a given border segment functions as an external boundary between the Schengen Area and non-Schengen territories.
-
D.
supportsSchengenFlights
Indicates that an entity enables or accommodates flights operating within the Schengen Area.
-
E.
haveSimilarStatusInSchengen
Indicates that two entities share a comparable legal or administrative status within the Schengen 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5776b848190bfe3a06ff261dc31 |
completed | April 15, 2026, 12:01 a.m. |
| PD | Predicate disambiguation | batch_69de8c1798c08190b433e9ad21e41a42 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:55 a.m.