Triple

T1910446
Position Surface form Disambiguated ID Type / Status
Subject Dan Klecko E38096 entity
Predicate familyName P18 FINISHED
Object Klecko E38096 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: Klecko | Statement: [Dan Klecko, familyName, Klecko]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Klecko
Context triple: [Dan Klecko, familyName, Klecko]
  • A. Klecko chosen
    Klecko is the surname of former American football defensive lineman Joe Klecko, best known for his standout career with the New York Jets as part of the “New York Sack Exchange.”
  • B. Smolikas
    Smolikas is a prominent mountain in northern Greece, known as the second-highest peak in the country after Mount Olympus.
  • C. Krieblowitz
    Krieblowitz was a village in Silesia (now Krobielowice, Poland) historically notable as the estate and place of death of Prussian field marshal Gebhard Leberecht von Blücher.
  • D. Grocka
    Grocka is a suburban municipality of Belgrade in Serbia, known for its agricultural production, especially fruit growing, and its location along the Danube River.
  • E. Konerko
    Konerko is the surname of Paul Konerko, a former Major League Baseball first baseman best known for his long tenure and leadership with the Chicago White Sox.
  • 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_69a8862a26088190aae5243695aeefc0 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1b88db48190a9229a7416054a85 completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3d5972881908856b75b324a1ad2 completed March 8, 2026, 10:10 p.m.
Created at: March 4, 2026, 7:35 p.m.