Triple

T3943769
Position Surface form Disambiguated ID Type / Status
Subject Ulster Cup E92095 entity
Predicate region P40 FINISHED
Object Ulster E41718 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: Ulster | Statement: [Ulster Cup, region, Ulster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ulster
Context triple: [Ulster Cup, region, Ulster]
  • A. Ulster chosen
    Ulster is a historic province in the north of Ireland, encompassing parts of both Northern Ireland and the Republic of Ireland, known for its distinct cultural and political identity.
  • B. Northern Ireland
    Northern Ireland is a constituent country of the United Kingdom located in the northeast of the island of Ireland, known for its distinct cultural heritage, complex political history, and capital city, Belfast.
  • C. Leinster
    Leinster is a province in eastern Ireland that includes the capital city, Dublin, and is the country’s most populous region.
  • D. New Ireland
    New Ireland is a long, narrow island in the Bismarck Archipelago known for its distinctive Malagan art and as one of the major island provinces of Papua New Guinea.
  • E. Munster
    Munster is a small town in the Grand Est region of northeastern France, known for its namesake strong-smelling cheese and picturesque setting in the Vosges mountains.
  • 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_69aed965502c8190904ebad1203a4ae8 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeee00d94881908fcf5ee1e27b1658 completed March 9, 2026, 3:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69b556173f848190bf8b879a7c61a43f completed March 14, 2026, 12:35 p.m.
Created at: March 9, 2026, 3:24 p.m.