Triple

T8975678
Position Surface form Disambiguated ID Type / Status
Subject Claudia Larson E214380 entity
Predicate hasRelative P367 FINISHED
Object Walter unclear NED1 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: [Claudia Larson, hasRelative, Walter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Walter
Context triple: [Claudia Larson, hasRelative, Walter]
  • A. Walter
    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. 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.
  • E. Walter Forward
    Walter Forward was a 19th-century American politician and lawyer who served as U.S. Secretary of the Treasury under President John Tyler.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide. chosen

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_69ca839ea8b88190922c6a326ffcc0d3 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6784d1808190899c980f76084ff8 completed April 1, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc96aa46c81908b95a23fd2b4da57 completed April 3, 2026, 2:06 p.m.
Created at: March 30, 2026, 7:02 p.m.