Triple

T2481547
Position Surface form Disambiguated ID Type / Status
Subject Guido van Rossum E55827 entity
Predicate spouse P13 FINISHED
Object Kim Knapp E55827 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: Kim Knapp | Statement: [Guido van Rossum, spouse, Kim Knapp]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kim Knapp
Context triple: [Guido van Rossum, spouse, Kim Knapp]
  • A. Kim Knapp chosen
    Kim Knapp is known as the spouse of Guido van Rossum, the creator of the Python programming language.
  • B. Kim Parker
    Kim Parker is a comedic, outspoken teenage character from the sitcom "Moesha," later becoming a central figure in its spin-off series "The Parkers."
  • C. Kim Keever
    Kim Keever is an American artist and photographer known for his large-scale, otherworldly landscape images created by photographing paint and materials suspended in water.
  • D. Katherine Rogers
    Katherine Rogers was the mother of John Harvard, the English clergyman whose bequest helped found Harvard College in colonial Massachusetts.
  • E. Barrie Chase
    Barrie Chase is an American actress and dancer best known for her work in 1950s–60s Hollywood films and television, including frequent collaborations with Fred Astaire.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd161bf3c8190834502968180e9cf completed March 7, 2026, 7:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17b48d0881909442717d318a6f05 completed March 9, 2026, 6:55 p.m.
Created at: March 6, 2026, 9:45 p.m.