Triple

T16467943
Position Surface form Disambiguated ID Type / Status
Subject King K. Rool E399982 entity
Predicate species P87 FINISHED
Object Kremling E370304 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: Kremling | Statement: [King K. Rool, species, Kremling]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kremling
Context triple: [King K. Rool, species, Kremling]
  • A. Kremlings chosen
    Kremlings are a recurring race of crocodilian villains in the Donkey Kong video game series, often serving as the primary antagonists led by King K. Rool.
  • B. Vladimir Putin
    Vladimir Putin is the long-serving Russian leader and former KGB officer who has played a central and often controversial role in post-Soviet Russian politics and global affairs.
  • C. Krasnoselsky
    Krasnoselsky is the former name of what is now known as Sokolniki station in the Moscow Metro system.
  • D. Godunov
    Godunov is a Russian surname most famously associated with Boris Godunov, the tsar who ruled Russia at the turn of the 17th century.
  • E. Gus-Khrustalny
    Gus-Khrustalny is a town in western Russia known for its historic glass-making industry and status as a local industrial center.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32dcd707081908fb7ca91a8c09e0a completed April 18, 2026, 7:07 a.m.
NED1 Entity disambiguation (via context triple) batch_6a004f5914dc81908c3b8cf999ee76a1 completed May 10, 2026, 9:26 a.m.
Created at: April 10, 2026, 5:11 a.m.