Triple

T14724925
Position Surface form Disambiguated ID Type / Status
Subject Gressy E345913 entity
Predicate proximityTo P350 FINISHED
Object Charles de Gaulle Airport E2173 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: Charles de Gaulle Airport | Statement: [Gressy, proximityTo, Charles de Gaulle Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charles de Gaulle Airport
Context triple: [Gressy, proximityTo, Charles de Gaulle Airport]
  • A. Charles de Gaulle Airport chosen
    Charles de Gaulle Airport is the largest international airport in France and a major European aviation hub serving the Paris metropolitan area.
  • 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. Roland Garros Airport
    Roland Garros Airport is the main international airport on the French island of Réunion in the Indian Ocean.
  • D. Paris Orly Airport
    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.
  • E. Toulouse-Blagnac Airport
    Toulouse-Blagnac Airport is an international airport serving the city of Toulouse in southwestern France and acting as a major hub for both commercial flights and the aerospace industry.
  • 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_69d822e5911c8190ba589f957dbd9ba7 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec25e9a14819081fa06fc601f295d completed April 14, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe24ac17888190bda346df75d37620 completed May 8, 2026, 6 p.m.
Created at: April 10, 2026, 1:29 a.m.