Triple

T11357216
Position Surface form Disambiguated ID Type / Status
Subject Jasper E268987 entity
Predicate hasVariant P455 FINISHED
Object Kaspar E157035 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: Kaspar | Statement: [Jasper, hasVariant, Kaspar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kaspar
Context triple: [Jasper, hasVariant, Kaspar]
  • A. Kasper
    Kasper is a surname most notably associated with former American football wide receiver Kevin Kasper.
  • B. Caspar chosen
    Caspar is one of the Three Wise Men in Christian tradition, often depicted as a king who visited the infant Jesus bearing gifts.
  • C. Karl
    Karl is the given first name of Charles Proteus Steinmetz, the renowned German-American mathematician and electrical engineer who revolutionized the understanding of alternating current systems.
  • D. Karl
    Karl Schwarzschild was a German physicist and astronomer best known for providing the first exact solution to Einstein’s field equations, leading to the concept of the Schwarzschild black hole.
  • E. Karl
    Karl is a ruthless, long-haired German terrorist and Hans Gruber’s vengeful right-hand man in the action film "Die Hard."
  • 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_69d6aacbe18081909e5fadb50082dd96 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7ea419afc8190b3a93141d015ebdf completed April 9, 2026, 6:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5564d5a8c81908bddbf3f771370f7 completed April 19, 2026, 10:25 p.m.
Created at: April 8, 2026, 9:33 p.m.