Triple

T15240935
Position Surface form Disambiguated ID Type / Status
Subject EuroMillions E364251 entity
Predicate participatingCountry P1059 FINISHED
Object Luxembourg E1844 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: Luxembourg | Statement: [EuroMillions, participatingCountry, Luxembourg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luxembourg
Context triple: [EuroMillions, participatingCountry, Luxembourg]
  • A. Luxembourg chosen
    Luxembourg is a small, landlocked Western European country known for its prosperous economy, status as a major financial center, and role as a founding member of the European Union.
  • B. Luxemburg
    Luxemburg is a surname most famously associated with Rosa Luxemburg, the Marxist theorist, revolutionary socialist, and co-founder of the Spartacist League in Germany.
  • C. Lichtenstein
    Lichtenstein is a surname most famously associated with Roy Lichtenstein, the American pop artist known for his comic-strip-inspired paintings.
  • D. Lichtenstein
    Lichtenstein is a municipality in the district of Reutlingen in the German state of Baden-Württemberg, known for the nearby Lichtenstein Castle.
  • E. Belgium and Luxembourg
    Belgium and Luxembourg are neighboring Western European countries that share a close historical, economic, and cultural relationship within the Benelux union.
  • 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_69d85a0dde7481908fc64d1e82d5d20d completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007db9a148190aadea8d5f8b6b261 completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69feef66b4f08190a072332123253166 completed May 9, 2026, 8:25 a.m.
Created at: April 10, 2026, 3:13 a.m.