Triple

T13023818
Position Surface form Disambiguated ID Type / Status
Subject Hódmezővásárhely E326247 entity
Predicate hasTwinTown P919 FINISHED
Object Zenta E131035 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: Zenta | Statement: [Hódmezővásárhely, hasTwinTown, Zenta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zenta
Context triple: [Hódmezővásárhely, hasTwinTown, Zenta]
  • A. Zenta chosen
    Zenta is a historic town in northern Serbia, best known as the site of a decisive 1697 battle between the Habsburg Monarchy and the Ottoman Empire.
  • B. Zator
    Zator is a small historic town in southern Poland known for its medieval heritage and proximity to major tourist attractions in Lesser Poland.
  • C. Zemba
    Zemba is a Bantu language variety spoken primarily in southwestern Angola and northern Namibia, closely related to and often considered a dialect of Herero.
  • D. Altran
    Altran is a global engineering and R&D services company, now operating as part of Capgemini Engineering after its acquisition by Capgemini.
  • E. Vacone
    Vacone is a small historic hilltop village in the Lazio region of central Italy, known for its scenic countryside and traditional rural character.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ed05e9c8190a4f208662bca0602 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c11c1f6c8190be1c570a7e44a313 completed May 3, 2026, 3:29 a.m.
Created at: April 9, 2026, 8:52 p.m.