Triple

T581083
Position Surface form Disambiguated ID Type / Status
Subject Paris airport system E15058 entity
Predicate hasMajorComponent P15759 FINISHED
Object Paris Orly Airport E46790 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: Paris Orly Airport | Statement: [Paris airport system, hasMajorComponent, Paris Orly Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris Orly Airport
Context triple: [Paris airport system, hasMajorComponent, Paris Orly Airport]
  • A. Paris Orly Airport chosen
    Paris Orly Airport is a major international airport serving the Paris metropolitan area, located south of the city and handling a large share of its domestic and European flights.
  • B. Paris–Le Bourget Airport
    Paris–Le Bourget Airport is a historic airport near Paris that now primarily serves business aviation and hosts the biennial Paris Air Show.
  • C. Charles de Gaulle Airport
    Charles de Gaulle Airport is the largest international airport in France and a major European aviation hub serving the Paris metropolitan area.
  • D. Strasbourg Airport
    Strasbourg Airport is an international airport serving the city of Strasbourg and the surrounding Alsace region in northeastern France.
  • E. Roissy-en-France
    Roissy-en-France is a commune in the northeastern suburbs of Paris best known for hosting most of Charles de Gaulle Airport, France’s largest international air hub.
  • 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_69a4935783b8819082b77726ec10cc42 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a49d2a5f5481908bb9a71ff0f534d4 completed March 1, 2026, 8:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a56c4973c88190a7d234f480ecade5 completed March 2, 2026, 10:54 a.m.
Created at: March 1, 2026, 7:33 p.m.