Triple

T11757969
Position Surface form Disambiguated ID Type / Status
Subject Catherine of Cleves E279574 entity
Predicate regionAssociatedWith P2830 FINISHED
Object Cleves E102459 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: Cleves | Statement: [Catherine of Cleves, regionAssociatedWith, Cleves]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cleves
Context triple: [Catherine of Cleves, regionAssociatedWith, Cleves]
  • A. Cleves chosen
    Cleves is a historic town in western Germany near the Dutch border, known for its medieval castle and role as a former ducal capital in the Lower Rhine region.
  • B. Dresden, Ohio
    Dresden, Ohio is a small village in Muskingum County known historically as the original home of the Longaberger Company and its handcrafted baskets.
  • C. Lorain
    Lorain is an industrial city on Lake Erie in northern Ohio, historically known for its steel production and shipbuilding.
  • D. Havana, Ohio
    Havana, Ohio is a small unincorporated community located in Huron County in north-central Ohio.
  • E. Dublin, Ohio
    Dublin, Ohio is a suburban city northwest of Columbus known for its affluent neighborhoods, strong school system, and annual Dublin Irish Festival.
  • 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a5220f148190ae60d1941a579ab6 completed April 10, 2026, 7:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8b788308190bd9c3b68569bc56a completed May 3, 2026, 2:53 a.m.
Created at: April 8, 2026, 9:41 p.m.