Triple

T19965273
Position Surface form Disambiguated ID Type / Status
Subject LIMJ E479914 entity
Predicate alsoKnownAs P39 FINISHED
Object Genoa Airport NE NERFINISHED

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: Genoa Airport | Statement: [LIMJ, alsoKnownAs, Genoa Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Genoa Airport
Context triple: [LIMJ, alsoKnownAs, Genoa Airport]
  • A. Genoa Cristoforo Colombo Airport chosen
    Genoa Cristoforo Colombo Airport is the main international airport serving the Italian port city of Genoa, providing passenger and cargo connections to domestic and European destinations.
  • B. Pisa International Airport
    Pisa International Airport is a major airport in Tuscany, Italy, serving as the primary air gateway to the city of Pisa and the surrounding region, including the nearby Leaning Tower tourist area.
  • C. Franca Airport
    Franca Airport is a regional public airport serving the city of Franca in the state of São Paulo, Brazil.
  • D. Giuseppe Verdi Airport
    Giuseppe Verdi Airport is a regional airport serving the city of Parma in northern Italy, named after the famed Italian composer.
  • E. Turin Airport
    Turin Airport is the main international airport serving the city of Turin and the surrounding Piedmont region in northern Italy.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e523c19881909f9197037200dde6 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e65bc4f47c8190a721f5e488150d81 completed April 20, 2026, 5 p.m.
Created at: April 10, 2026, 1:54 p.m.