Triple

T4499000
Position Surface form Disambiguated ID Type / Status
Subject Antony station E100770 entity
Predicate connectsTo P845 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: [Antony station, connectsTo, Paris–Orly Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Paris–Orly Airport
Context triple: [Antony station, connectsTo, 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_69bd43cdf15081909a4fa2585ff63b3e completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd56c29868819097633c7cd398e865 completed March 20, 2026, 2:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69be43821d5c8190b50e3eadabf2845b completed March 21, 2026, 7:06 a.m.
Created at: March 20, 2026, 1 p.m.