Triple

T20672666
Position Surface form Disambiguated ID Type / Status
Subject M3 motorway (Hungary) E508068 entity
Predicate passesNear P416 FINISHED
Object Füzesabony NE NERFINISHED

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: Füzesabony | Statement: [M3 motorway (Hungary), passesNear, Füzesabony]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Füzesabony
Context triple: [M3 motorway (Hungary), passesNear, Füzesabony]
  • A. Füzesabony chosen
    Füzesabony is a small town in northeastern Hungary known as a regional railway junction and gateway to the Bükk and Mátra regions.
  • B. Fertőd
    Fertőd is a small town in northwestern Hungary best known for the grand Esterházy Palace, often called the “Hungarian Versailles.”
  • C. Nagyvázsony
    Nagyvázsony is a village in Veszprém County, Hungary, known for its historic Kinizsi Castle and traditional rural character.
  • D. Nagykőrös
    Nagykőrös is a historic town in central Hungary known for its agricultural traditions and small-town character.
  • E. Törökbálint
    Törökbálint is a town in Pest County, Hungary, located just southwest of Budapest and known as a suburban residential area with growing commercial and industrial zones.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c1164881909a3bf1e3ddb2bc32 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6b5cb1fc88190805f623e93a70368 completed April 20, 2026, 11:24 p.m.
Created at: April 16, 2026, 11:44 a.m.