Triple

T16247723
Position Surface form Disambiguated ID Type / Status
Subject Virgil Blessing E394417 entity
Predicate associatedWith P37 FINISHED
Object Bo Decker E1201787 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: Bo Decker | Statement: [Virgil Blessing, associatedWith, Bo Decker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bo Decker
Context triple: [Virgil Blessing, associatedWith, Bo Decker]
  • A. Bo Decker chosen
    Bo Decker is the brash, naive young cowboy who serves as the central romantic figure in William Inge’s play "Bus Stop."
  • B. Paul Kersey
    Paul Kersey is the vigilante protagonist of the "Death Wish" film series, known for taking the law into his own hands after personal tragedy.
  • C. Clint Reilly
    Clint Reilly is a San Francisco-based political consultant, real estate investor, and former newspaper owner known for his influence in local politics and media.
  • D. Chris DeWolfe
    Chris DeWolfe is an American entrepreneur best known as the co-creator and former CEO of the pioneering social networking site MySpace.
  • E. Jordan Baxter
    Jordan Baxter is a fictional character portrayed by actor Graham Phillips, best known from his work in film and television.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e245942460819080897afad0d2fe09 completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a001f8ae4288190b59e4af3e3d95000 completed May 10, 2026, 6:02 a.m.
Created at: April 10, 2026, 5:04 a.m.