Triple

T11704061
Position Surface form Disambiguated ID Type / Status
Subject Felvidék E278196 entity
Predicate containsHistoricalCity P43430 FINISHED
Object Eperjes E523394 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: Eperjes | Statement: [Felvidék, containsHistoricalCity, Eperjes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eperjes
Context triple: [Felvidék, containsHistoricalCity, Eperjes]
  • A. Eperjes chosen
    Eperjes is a historic town in present-day Slovakia, known today as Prešov, which was one of the major urban centers of the former Upper Hungary region.
  • B. Ercsi
    Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
  • C. Mundruczó
    Mundruczó is the surname of Hungarian film and theatre director Kornél Mundruczó, known for his innovative and often provocative works.
  • D. Fajsz
    Fajsz was a 10th-century Grand Prince of the early Hungarian state, known primarily from medieval chronicles as one of the Árpád dynasty rulers.
  • E. Ezerjó
    Ezerjó is a traditional Hungarian white grape variety known for producing fresh, high-acid wines, particularly associated with the Mór wine region.
  • 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_69d6aaff2ce88190b4a1e4b341ad5377 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a49b1080819096593733ee48a187 completed April 10, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef83525ae081909ee6f3fbb5d37dd7 completed April 27, 2026, 3:40 p.m.
Created at: April 8, 2026, 9:40 p.m.