Triple

T8018095
Position Surface form Disambiguated ID Type / Status
Subject Sümeg E186670 entity
Predicate administrativeDivision P747 FINISHED
Object Tapolca District E512438 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: Tapolca District | Statement: [Sümeg, administrativeDivision, Tapolca District]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tapolca District
Context triple: [Sümeg, administrativeDivision, Tapolca District]
  • A. Tapolca District chosen
    Tapolca District is an administrative district in western Hungary, centered on the town of Tapolca and known for its scenic landscapes near Lake Balaton.
  • B. Paucarpata District
    Paucarpata District is an urban district in the Arequipa Province of southern Peru, situated on the slopes near the Misti volcano.
  • C. Ilava District
    Ilava District is an administrative district in the Trenčín Region of northwestern Slovakia, known for its mix of industrial towns and rural communities.
  • D. Marzoll district
    Marzoll district is a locality within the spa town of Bad Reichenhall in Bavaria, Germany, known for its scenic Alpine setting near the Austrian border.
  • E. Lince District
    Lince District is a centrally located urban district of Lima, Peru, known for its residential neighborhoods, commercial areas, and proximity to the city’s historic and financial centers.
  • 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_69ca82ac7fc081909b1398cf025423af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3df626e8819098a9f8908dfdad3b completed March 31, 2026, 3:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56c213ec8190b3bd96c42d1357e4 completed March 31, 2026, 11:20 p.m.
Created at: March 30, 2026, 5:20 p.m.