Triple

T1374590
Position Surface form Disambiguated ID Type / Status
Subject Bohemia E30190 entity
Predicate containsCity P294 FINISHED
Object Plzeň E19529 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: Plzeň | Statement: [Bohemia, containsCity, Plzeň]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Plzeň
Context triple: [Bohemia, containsCity, Plzeň]
  • A. Plzeň chosen
    Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
  • B. Ústí nad Labem
    Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
  • C. Ostrava
    Ostrava is a major industrial and cultural city in the northeastern Czech Republic, near the borders with Poland and Slovakia.
  • D. Hradec Králové
    Hradec Králové is a historic city in the Czech Republic known for its educational institutions, modernist architecture, and role as a regional cultural and economic center.
  • E. Zlín
    Zlín is a city in the Czech Republic known for its modernist architecture and historical association with the Baťa shoe company.
  • 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_69a498f912008190a376a98b207b2071 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c2f7aeb08190b52ef1058c18327e completed March 1, 2026, 10:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad58abdeb4819097000c1b2cf19538 completed March 8, 2026, 11:08 a.m.
Created at: March 1, 2026, 7:57 p.m.