Triple
T15866370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vergiate |
E384722
|
entity |
| Predicate | distanceToMilanMalpensaAirport |
P92564
|
FINISHED |
| Object | approximately 10–15 km |
—
|
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 10–15 km | Statement: [Vergiate, distanceToMilanMalpensaAirport, approximately 10–15 km]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: distanceToMilanMalpensaAirport Context triple: [Vergiate, distanceToMilanMalpensaAirport, approximately 10–15 km]
-
A.
distanceFromMilan
Indicates the spatial distance between a given entity and the city of Milan.
-
B.
distanceToMBBAirport_km
chosen
Indicates the distance, measured in kilometers, from a given location to the MBB airport.
-
C.
otherMilanAirports
Indicates that the related entity is an airport in Milan other than the primary or main Milan airport under consideration.
-
D.
distanceToFrankfurtAirport_km
Indicates the physical distance, measured in kilometers, between a given location and Frankfurt Airport.
-
E.
distanceFromPavia
Indicates the spatial distance between a given entity or location and the city of Pavia.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
Created at: April 10, 2026, 4:50 a.m.