Triple

T4593447
Position Surface form Disambiguated ID Type / Status
Subject European Union administrative centres E103550 entity
Predicate hasMember P10 FINISHED
Object Tallinn E20558 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: Tallinn | Statement: [European Union administrative centres, hasMember, Tallinn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tallinn
Context triple: [European Union administrative centres, hasMember, Tallinn]
  • A. Tallinn chosen
    Tallinn is the capital and largest city of Estonia, a historic Baltic Sea port known for its well-preserved medieval Old Town and strategic maritime location.
  • B. Tartu
    Tartu is Estonia’s second-largest city and a historic cultural and intellectual center, best known as the country’s main university town.
  • C. Helsinki
    Helsinki is the capital and largest city of Finland, known for its coastal location on the Baltic Sea, modern design, and vibrant cultural life.
  • D. Riga
    Riga is a town in the Sitamarhi district of the Indian state of Bihar.
  • E. Riga
    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.
  • 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_69bd43dccaf08190aa89e9991a289719 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd593cf8508190acfc6ddb5716e80a completed March 20, 2026, 2:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde0d553f08190a56020e1bf8f700d completed March 21, 2026, 12:05 a.m.
Created at: March 20, 2026, 1:11 p.m.