Triple

T19512065
Position Surface form Disambiguated ID Type / Status
Subject Dunántúl E488179 entity
Predicate containsCity P294 FINISHED
Object Dunaújváros 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: Dunaújváros | Statement: [Dunántúl, containsCity, Dunaújváros]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dunaújváros
Context triple: [Dunántúl, containsCity, Dunaújváros]
  • A. Dunaújváros chosen
    Dunaújváros is an industrial city in central Hungary known for its steel production and post-war socialist urban planning.
  • B. Dunakeszi
    Dunakeszi is a town in Hungary located just north of Budapest, known as a rapidly growing suburban and commuter settlement along the Danube in Pest County.
  • C. Dombóvár
    Dombóvár is a town in southern Hungary known as an important local transport and economic center within Tolna County.
  • D. Tiszaújváros
    Tiszaújváros is an industrial town in northeastern Hungary known for its large chemical and energy industries and its location along the Tisza River.
  • E. 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.
  • 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_69d8e8da8bec819081f400199491ccc3 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6359908fc8190bd05f26d4271d268 completed April 20, 2026, 2:18 p.m.
Created at: April 10, 2026, 1:40 p.m.