Triple

T10057733
Position Surface form Disambiguated ID Type / Status
Subject Gualtiero E208903 entity
Predicate equivalentNameInEnglish P3437 FINISHED
Object Walter E32053 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: Walter | Statement: [Gualtiero, equivalentNameInEnglish, Walter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walter
Context triple: [Gualtiero, equivalentNameInEnglish, Walter]
  • A. Walter chosen
    Walter is a masculine given name of Germanic origin that has been widely used in English-speaking countries.
  • B. Wilbert
    Wilbert is the given first name of American character actor Bill Cobbs, known for his numerous supporting roles in film and television.
  • C. Wally Fay
    Wally Fay is a supporting character in the 1945 film noir "Mildred Pierce," known as a somewhat sleazy businessman entangled in the story’s web of betrayal and murder.
  • D. Frank Worthington
    Frank Worthington was an English professional footballer best known as a flamboyant forward who played for clubs such as Leicester City and Bolton Wanderers during the 1970s and 1980s.
  • E. Walter Nelson
    Walter Nelson was an attorney who served on the defense team in the landmark Ossian Sweet murder trial, which challenged racial injustice in 1920s Detroit.
  • 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfaf7700819084dedf7b63e789c1 completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a5d4b308190b5b1ece1ca99be86 completed April 5, 2026, 5:22 p.m.
Created at: March 30, 2026, 8:57 p.m.