Triple

T16340825
Position Surface form Disambiguated ID Type / Status
Subject Vanves E396795 entity
Predicate nearAirport P350 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: [Vanves, nearAirport, Paris-Orly Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris-Orly Airport
Context triple: [Vanves, nearAirport, 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_69d87f26864c819088365ca381a003c2 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2da09dcf48190b6fdd14b1812c56a completed April 18, 2026, 1:10 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00261cf0648190b4dd5ff79de7e315 completed May 10, 2026, 6:30 a.m.
Created at: April 10, 2026, 5:07 a.m.