Triple

T22867258
Position Surface form Disambiguated ID Type / Status
Subject Jugoplastika Split E567087 entity
Predicate alsoKnownAs P39 FINISHED
Object KK Split NE NERFINISHED

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: KK Split | Statement: [Jugoplastika Split, alsoKnownAs, KK Split]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: KK Split
Context triple: [Jugoplastika Split, alsoKnownAs, KK Split]
  • A. KK Split chosen
    KK Split is a Croatian professional basketball club based in Split, historically one of Europe’s most successful teams, especially known for its dominance in the late 1980s and early 1990s.
  • B. KK
    KK is a standardized orthography for the Cornish language known as Kernewek Kemmyn.
  • C. KK
    KK is the commonly used abbreviation for the Karachi Kings, a professional franchise cricket team in Pakistan's Super League.
  • D. KK
    KK is the commonly used abbreviation for Kota Kinabalu, the capital city of Sabah in Malaysian Borneo known for its coastal setting and proximity to Mount Kinabalu.
  • E. KK
    KK is a key spirit detective and mentor figure in the action-adventure game Ghostwire: Tokyo, guiding the protagonist with his supernatural expertise.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e24589083081908d5694c4fdc80086 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17f0210b481908e0e6c4f95ba5a3b completed April 29, 2026, 3:46 a.m.
Created at: April 17, 2026, 3:38 p.m.