Triple

T436388
Position Surface form Disambiguated ID Type / Status
Subject Brussels E10018 entity
Predicate hasAirport P105 FINISHED
Object Brussels Airport E36797 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: Brussels Airport | Statement: [Brussels, hasAirport, Brussels Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Brussels Airport
Context triple: [Brussels, hasAirport, Brussels Airport]
  • A. Brussels Airport chosen
    Brussels Airport is the main international airport serving Brussels and one of Belgium’s busiest air transport hubs for passengers and cargo.
  • B. Brussels South Charleroi Airport
    Brussels South Charleroi Airport is a major low-cost international airport in Belgium, widely used by budget airlines and serving as an alternative to Brussels Airport.
  • C. Amsterdam Airport Schiphol
    Amsterdam Airport Schiphol is the main international airport of the Netherlands and one of Europe’s busiest aviation hubs for passenger and cargo traffic.
  • 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. Hamburg Airport
    Hamburg Airport is an international airport in northern Germany serving the city of Hamburg and the surrounding region as a major passenger and cargo 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef0c97188190b62104cb639d4b60 completed Feb. 28, 2026, 1:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69a431e6989c81909d0a79408f8ca76a completed March 1, 2026, 12:32 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.