Triple
T11647194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vienna International Airport |
E276805
|
entity |
| Predicate | distanceFromViennaCityCentre |
P88395
|
FINISHED |
| Object | approximately 18 km southeast |
—
|
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: approximately 18 km southeast | Statement: [Vienna International Airport, distanceFromViennaCityCentre, approximately 18 km southeast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceFromViennaCityCentre Context triple: [Vienna International Airport, distanceFromViennaCityCentre, approximately 18 km southeast]
-
A.
distanceFromVienna
chosen
Indicates the spatial distance between a given entity’s location and the city of Vienna.
-
B.
distanceFromBratislava_km
Indicates the distance, measured in kilometers, between a given entity’s location and the city of Bratislava.
-
C.
distanceToBudapest_km
Indicates the physical distance, measured in kilometers, between a given location and Budapest.
-
D.
distanceFromPragueKmApprox
Indicates an approximate distance, measured in kilometers, between a given entity and the city of Prague.
-
E.
distanceFromStockholmCityCentre
Indicates the measured distance between a given location and the center of Stockholm city.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a2cd9bb0819093d107204bed2fe0 |
completed | April 10, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d85dd94bdc819091fa2ed33eb31624 |
completed | April 10, 2026, 2:18 a.m. |
Created at: April 8, 2026, 9:39 p.m.