Triple

T1311950
Position Surface form Disambiguated ID Type / Status
Subject Białystok E28010 entity
Predicate twinCity P1072 FINISHED
Object Riga E20805 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: Riga | Statement: [Białystok, twinCity, Riga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riga
Context triple: [Białystok, twinCity, Riga]
  • A. Riga chosen
    Riga is the capital and largest city of Latvia, a historic cultural and economic hub on the Baltic Sea known for its Art Nouveau architecture and significant port.
  • B. Daugavpils
    Daugavpils is Latvia’s second-largest city, known as the birthplace of abstract expressionist painter Mark Rothko and for its multicultural heritage and 19th-century fortress.
  • C. Liepāja, Latvia
    Liepāja is a major port city on Latvia’s Baltic Sea coast, known for its historic architecture, naval heritage, and cultural life.
  • D. Ventspils
    Ventspils is a port city on Latvia’s Baltic Sea coast known for its major ice-free harbor, oil and cargo terminals, and well-preserved historic center.
  • E. Jelgava
    Jelgava is a city in central Latvia known for its historic Jelgava Palace and role as a regional cultural and educational center.
  • 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_69a498532c3481909223b74af2e578df completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c1572b1c8190ab978198c2d655c8 completed March 1, 2026, 10:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69acbaeeece08190a805a2f037e709ce completed March 7, 2026, 11:55 p.m.
Created at: March 1, 2026, 7:55 p.m.