Triple

T14764711
Position Surface form Disambiguated ID Type / Status
Subject Elizabeth de Burgh, 4th Countess of Ulster E346962 entity
Predicate landholdings P10159 FINISHED
Object Connacht E61577 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: Connacht | Statement: [Elizabeth de Burgh, 4th Countess of Ulster, landholdings, Connacht]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Connacht
Context triple: [Elizabeth de Burgh, 4th Countess of Ulster, landholdings, Connacht]
  • A. Connacht chosen
    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.
  • B. Leinster
    Leinster is a province in eastern Ireland that includes the capital city, Dublin, and is the country’s most populous region.
  • 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_69d822e8896c819091169882f9b20486 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7f3a1608190b1b17624003a0c7f completed April 14, 2026, 11:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cf4cef081909fa62125f43b36bc completed May 8, 2026, 4:19 p.m.
Created at: April 10, 2026, 1:30 a.m.