Triple

T10741511
Position Surface form Disambiguated ID Type / Status
Subject Croke Park E253336 entity
Predicate province P604 FINISHED
Object Leinster E51255 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: Leinster | Statement: [Croke Park, province, Leinster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Leinster
Context triple: [Croke Park, province, Leinster]
  • A. Leinster chosen
    Leinster is a province in eastern Ireland that includes the capital city, Dublin, and is the country’s most populous region.
  • B. Connacht
    Connacht is one of the four traditional provinces of Ireland, located in the west of the island and historically known for its Gaelic culture and rugged landscapes.
  • C. Munster
    Munster is a town in Lower Saxony, Germany, known for its military training areas and location within the Lüneburg Heath region.
  • D. Munster
    Munster is a historic province in the south of Ireland, known for its major role in Irish history, culture, and conflicts, including the 17th-century wars.
  • 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d7104446288190800253f8b652f710 completed April 9, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69de22fc13b0819098caf88328397053 completed April 14, 2026, 11:20 a.m.
Created at: April 8, 2026, 9:15 p.m.