Triple

T18246578
Position Surface form Disambiguated ID Type / Status
Subject Speaker of the Saeima E436966 entity
Predicate locationOfSeat P23175 FINISHED
Object Riga 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: Riga | Statement: [Speaker of the Saeima, locationOfSeat, Riga]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riga
Context triple: [Speaker of the Saeima, locationOfSeat, 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 (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_69d8b91104e08190a8241f7d260a5162 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4f7e7d49c8190b227a13b63615754 completed April 19, 2026, 3:42 p.m.
Created at: April 10, 2026, 10:33 a.m.