Triple
T25214238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonio Canova Airport |
E631779
|
entity |
| Predicate | distanceToVeniceApproxKm |
P74119
|
FINISHED |
| Object | 30 |
—
|
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: 30 | Statement: [Antonio Canova Airport, distanceToVeniceApproxKm, 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToVeniceApproxKm Context triple: [Antonio Canova Airport, distanceToVeniceApproxKm, 30]
-
A.
distanceToVenice_km
chosen
Indicates the physical distance, measured in kilometers, between a given place and the city of Venice.
-
B.
distanceToSavona_km
Indicates the physical distance, measured in kilometers, between a given entity’s location and the city of Savona.
-
C.
distanceToTrevisoKilometers
Indicates the physical distance, measured in kilometers, between a given location or entity and the city of Treviso.
-
D.
distanceToGenoa_km
Indicates the physical distance, measured in kilometers, between a given place or object and the city of Genoa.
-
E.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
- 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_69e75a8d1aa48190a4320acd3654762c |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f71422adac8190a5ceb32dcf820833 |
completed | May 3, 2026, 9:23 a.m. |
| PD | Predicate disambiguation | batch_69f712764d2c819081b64b27e5de4a13 |
completed | May 3, 2026, 9:16 a.m. |
Created at: April 21, 2026, 12:58 p.m.