Triple

T4063985
Position Surface form Disambiguated ID Type / Status
Subject Riga City Hall E86280 entity
Predicate hasMunicipality P847 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: [Riga City Hall, hasMunicipality, Riga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riga
Context triple: [Riga City Hall, hasMunicipality, Riga]
  • A. Riga
    Riga is a town in the Sitamarhi district of the Indian state of Bihar.
  • B. 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.
  • C. 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.
  • D. Valmiera
    Valmiera is a historic city in northern Latvia, situated on the Gauja River and known today as a regional economic and cultural center in the Vidzeme region.
  • E. Liepāja, Latvia
    Liepāja is a major port city on Latvia’s Baltic Sea coast, known for its historic architecture, naval heritage, and cultural life.
  • 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_69aed93c69208190a4efac0efe3cd69b completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbd7896c81909c61ed0d910d9c5f completed March 9, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b55b5388190a90551c43388f3fc completed March 14, 2026, 2:06 p.m.
Created at: March 9, 2026, 3:38 p.m.