Triple

T14867221
Position Surface form Disambiguated ID Type / Status
Subject Budapest Ferenc Liszt International Airport E349646 entity
Predicate locatedNear P294 FINISHED
Object Ferihegy E1081666 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: Ferihegy | Statement: [Budapest Ferenc Liszt International Airport, locatedNear, Ferihegy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ferihegy
Context triple: [Budapest Ferenc Liszt International Airport, locatedNear, Ferihegy]
  • A. Kunhegyes
    Kunhegyes is a small town in Jász-Nagykun-Szolnok County in central Hungary, known for its rural character and agricultural surroundings.
  • B. Gellért-hegy
    Gellért-hegy is a prominent hill overlooking the Danube in Budapest, known for its panoramic city views, historic monuments, and the iconic Citadella fortress.
  • C. Nagyerdő
    Nagyerdő is a large, historic forested park and recreational area in Debrecen, Hungary, known for its natural beauty and cultural attractions.
  • D. Madárhegy chosen
    Madárhegy is a residential neighborhood in the hilly, green southwestern part of Budapest known for its newer housing developments and suburban atmosphere.
  • E. Węgierska Górka
    Węgierska Górka is a village in southern Poland’s Silesian Voivodeship, known as a popular Beskid mountain tourist resort and the site of significant World War II fortifications.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded5776b848190bfe3a06ff261dc31 completed April 15, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe651067cc8190b9c218ef1f802762 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:55 a.m.