Triple

T6977666
Position Surface form Disambiguated ID Type / Status
Subject Tatabánya E161753 entity
Predicate hasTwinTown P919 FINISHED
Object Dunaújváros E234840 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: Dunaújváros | Statement: [Tatabánya, hasTwinTown, Dunaújváros]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dunaújváros
Context triple: [Tatabánya, hasTwinTown, Dunaújváros]
  • A. Dunaújváros chosen
    Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
  • B. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • C. Gyulafehérvár
    Gyulafehérvár, known today as Alba Iulia in Romania, is a historic city that served as the political and cultural center of Transylvania for centuries.
  • D. Gödöllő
    Gödöllő is a Hungarian town near Budapest best known for its historic Royal Palace, one of the largest Baroque palaces in Hungary.
  • E. Veszprém
    Veszprém is a historic city in western Hungary known for its medieval castle district and role as a regional cultural and administrative center.
  • 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_69c68854a0d88190bc0bf82263f1afce completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db68d25c8190a1776908619ad979 completed March 27, 2026, 7:32 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a0ad57c81909aec9f619dc68bd7 completed March 28, 2026, 5:41 a.m.
Created at: March 27, 2026, 2:31 p.m.