Triple

T11453767
Position Surface form Disambiguated ID Type / Status
Subject Blawenburg Historic District E271469 entity
Predicate namedAfter P63 FINISHED
Object Blawenburg E522012 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: Blawenburg | Statement: [Blawenburg Historic District, namedAfter, Blawenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blawenburg
Context triple: [Blawenburg Historic District, namedAfter, Blawenburg]
  • A. Blawenburg, New Jersey chosen
    Blawenburg, New Jersey is an unincorporated historic village in Somerset County known for its rural character and 19th-century architecture.
  • B. Beffendorf
    Beffendorf is a village and district of the town Oberndorf am Neckar in the German state of Baden-Württemberg.
  • C. Hofstadt
    Hofstadt is the maiden surname of Betty Draper, a central character on the television series "Mad Men."
  • D. Cinnaminson
    Cinnaminson is a suburban township in Burlington County, New Jersey, located along the Delaware River and within the Philadelphia metropolitan area.
  • E. Waldstadt
    Waldstadt is a district of Karlsruhe in the German state of Baden-Württemberg, characterized by its forested setting and primarily residential layout.
  • 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_69d6aadff8888190a13f253f0d460874 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d81c7057688190ad8aa99426e4ca30 completed April 9, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5d3d740008190a05ccb789ac0906d completed April 20, 2026, 7:20 a.m.
Created at: April 8, 2026, 9:35 p.m.