Triple

T17496897
Position Surface form Disambiguated ID Type / Status
Subject Lobkovice E426083 entity
Predicate hasNearbyCity P350 FINISHED
Object Mělník 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: Mělník | Statement: [Lobkovice, hasNearbyCity, Mělník]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mělník
Context triple: [Lobkovice, hasNearbyCity, 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. Měčín
    Měčín is a small town in the Plzeň Region of the Czech Republic.
  • C. Osek
    Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
  • D. Třeboň
    Třeboň is a historic spa town in the Czech Republic renowned for its Renaissance architecture, fishpond landscapes, and long tradition of carp farming.
  • E. Broumov
    Broumov is a historic town in northeastern Bohemia, Czech Republic, known for its Benedictine monastery and proximity to the Broumov Walls sandstone rock formations.
  • 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_69d889dccf7481909264a1844a2e9100 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e4520e9c8c8190aa955766bc915d26 completed April 19, 2026, 3:54 a.m.
Created at: April 10, 2026, 5:48 a.m.