Triple

T16066510
Position Surface form Disambiguated ID Type / Status
Subject Orly area E389745 entity
Predicate hasFacility P105 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: [Orly area, hasFacility, Paris-Orly Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris-Orly Airport
Context triple: [Orly area, hasFacility, 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. Roland Garros Airport
    Roland Garros Airport is the main international airport on the French island of Réunion in the Indian Ocean.
  • E. Lyon–Saint-Exupéry Airport
    Lyon–Saint-Exupéry Airport is a major international airport in eastern France serving the city of Lyon and the surrounding Auvergne-Rhône-Alpes region.
  • 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e1837ca628819081dfc439fe322d58 completed April 17, 2026, 12:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00077c8e2881909a11a4c53691d187 completed May 10, 2026, 4:20 a.m.
Created at: April 10, 2026, 4:57 a.m.