Triple

T15250594
Position Surface form Disambiguated ID Type / Status
Subject Trenord E364507 entity
Predicate connectsTo P845 FINISHED
Object Milan Malpensa Airport E35167 NE 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: Milan Malpensa Airport | Statement: [Trenord, connectsTo, Milan Malpensa Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Milan Malpensa Airport
Context triple: [Trenord, connectsTo, Milan Malpensa Airport]
  • A. Milan Malpensa Airport chosen
    Milan Malpensa Airport is a major international airport serving the Milan metropolitan area in northern Italy and one of the country’s busiest air transport hubs.
  • B. Milan Linate Airport
    Milan Linate Airport is a major city airport serving Milan, Italy, primarily handling domestic and short-haul European flights close to the city center.
  • C. Milan Bergamo Airport
    Milan Bergamo Airport is a major low-cost international airport in northern Italy that serves the Milan metropolitan area and is a key base for Ryanair’s European operations.
  • D. Parma Airport
    Parma Airport is a regional airport in Parma, Italy, serving domestic and limited international flights for the Emilia-Romagna region.
  • E. Franca Airport
    Franca Airport is a regional public airport serving the city of Franca in the state of São Paulo, Brazil.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007f62b9c8190b9ad40e2d1912b63 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d3b3fc0819094daf892200bd1ac completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 3:13 a.m.