Triple

T10455088
Position Surface form Disambiguated ID Type / Status
Subject Munster E246530 entity
Predicate hasNameInFrench P6538 FINISHED
Object Munster E61441 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: Munster | Statement: [Munster, hasNameInFrench, Munster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Munster
Context triple: [Munster, hasNameInFrench, Munster]
  • A. Munster chosen
    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.
  • B. 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.
  • C. Munster
    Munster is a town in Lower Saxony, Germany, known for its military training areas and location within the Lüneburg Heath region.
  • D. Leinster
    Leinster is a province in eastern Ireland that includes the capital city, Dublin, and is the country’s most populous region.
  • E. 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.
  • 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_69d381c04fe08190957c26c526a3b05a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4fe48d15c8190bae0d4859e6cda5d completed April 7, 2026, 12:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69d87f10b45c81908f1d3128c65750f8 completed April 10, 2026, 4:39 a.m.
Created at: April 6, 2026, 12:17 p.m.