Triple

T19235934
Position Surface form Disambiguated ID Type / Status
Subject Walsenburg, Colorado E480994 entity
Predicate namedAfter P63 FINISHED
Object Fred Walsen NE NERFINISHED

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: Fred Walsen | Statement: [Walsenburg, Colorado, namedAfter, Fred Walsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fred Walsen
Context triple: [Walsenburg, Colorado, namedAfter, Fred Walsen]
  • A. Fred Walsen chosen
    Fred Walsen was an early settler and prominent local figure in southern Colorado whose influence and contributions led to the city of Walsenburg being named in his honor.
  • B. Wallace V. Friesen
    Wallace V. Friesen was a psychologist best known for co-developing, with Paul Ekman, influential systems for measuring and analyzing human facial expressions of emotion.
  • C. Harry Gullichsen
    Harry Gullichsen was a Finnish industrialist and prominent art patron known for commissioning modernist works, including Alvar Aalto’s celebrated Villa Mairea.
  • D. Harold Wenstrom
    Harold Wenstrom was an American cinematographer active during the early 20th century, known for his work on numerous silent and early sound films.
  • E. Ronald Wolfe
    Ronald Wolfe was a British television comedy writer best known for co-creating popular sitcoms such as "The Rag Trade."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8ccb8f48190ad420098e74fb1db completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e5faec6d0c8190b90cb1bb3160a847 completed April 20, 2026, 10:07 a.m.
Created at: April 10, 2026, 1:26 p.m.