Triple

T1597890
Position Surface form Disambiguated ID Type / Status
Subject Central Bohemian Region E34324 entity
Predicate hasHistoricTown P847 FINISHED
Object Mělník E231915 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: Mělník | Statement: [Central Bohemian Region, hasHistoricTown, Mělník]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mělník
Context triple: [Central Bohemian Region, hasHistoricTown, Mělník]
  • A. Mělník chosen
    Mělník is a historic Czech town north of Prague, known for its wine production and its location at the confluence of the Elbe and Vltava rivers.
  • B. Slaný
    Slaný is a historic town in the Czech Republic known for its medieval center and location northwest of Prague.
  • C. Říčany
    Říčany is a town in the Czech Republic, located just southeast of Prague and known as a popular residential and commuter suburb with historical roots.
  • D. Karviná
    Karviná is an industrial city in the Moravian-Silesian Region of the Czech Republic, historically part of Cieszyn Silesia and known for its coal mining heritage.
  • E. Vávrová
    Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
  • 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_69a885fdcb9c819081ce6f0b8cd477dd completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a9092f5f148190b987bc943e89e29c completed March 5, 2026, 4:40 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7ed1080081909a931e4d045edd83 completed March 9, 2026, 8:03 a.m.
Created at: March 4, 2026, 7:27 p.m.