Triple

T14262159
Position Surface form Disambiguated ID Type / Status
Subject TARGET2 E353547 entity
Predicate regionServed P82 FINISHED
Object euro area E2721 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: euro area | Statement: [TARGET2, regionServed, euro area]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: euro area
Context triple: [TARGET2, regionServed, euro area]
  • A. Eurozone chosen
    The Eurozone is the group of European Union countries that have adopted the euro as their common official currency and share a unified monetary policy.
  • B. Euro
    The Euro is the official common currency used by many countries in the European Union, facilitating trade and travel across much of Europe.
  • C. EURO
    EURO is the commonly used abbreviation for the World Health Organization’s Regional Office for Europe, which oversees public health initiatives across the European region.
  • D. EURO
    EURO is the commonly used short name for the UEFA European Championship, the premier international football tournament for national teams in Europe.
  • E. EU
    EU is the vehicle registration code for the German district of Euskirchen in the state of North Rhine-Westphalia.
  • 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_69d8278c43e08190824146f4632b89a5 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de63563fc88190b0abdbf8529c65eb completed April 14, 2026, 3:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd326367348190b4b31b32f4ca5639 completed May 8, 2026, 12:46 a.m.
Created at: April 10, 2026, 1:09 a.m.