Triple

T14975933
Position Surface form Disambiguated ID Type / Status
Subject Susanne Shore E373447 entity
Predicate spouse P13 FINISHED
Object Pete Ricketts E76083 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: Pete Ricketts | Statement: [Susanne Shore, spouse, Pete Ricketts]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pete Ricketts
Context triple: [Susanne Shore, spouse, Pete Ricketts]
  • A. Pete Ricketts chosen
    Pete Ricketts is an American businessman and Republican politician who served as the governor of Nebraska and later as a U.S. senator from the state.
  • B. Ben Sasse
    Ben Sasse is an American academic and politician who served as a U.S. Senator from Nebraska before becoming president of the University of Florida.
  • C. Nick Thune
    Nick Thune is an American stand-up comedian and actor known for his dry, absurdist humor and appearances in film and television.
  • D. Bob Engemann
    Bob Engemann was an American singer best known as one of the founding members of the pop vocal trio The Lettermen, popular in the 1960s for their smooth harmonies and romantic ballads.
  • E. Mike Donnelly
    Mike Donnelly is the well-meaning but accident-prone protagonist of the comedy film "Black Sheep," whose misadventures jeopardize his brother’s political campaign.
  • 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_69d85ccbbcd48190acb56e7cf104d8ad completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded6e8733081908e06b53746eb6eb6 completed April 15, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe9692e4fc8190a1194a41fc8a832c completed May 9, 2026, 2:06 a.m.
Created at: April 10, 2026, 2:51 a.m.