Triple

T19795059
Position Surface form Disambiguated ID Type / Status
Subject Magnus Manske E475518 entity
Predicate name P16 FINISHED
Object Magnus Manske 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: Magnus Manske | Statement: [Magnus Manske, name, Magnus Manske]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Magnus Manske
Context triple: [Magnus Manske, name, Magnus Manske]
  • A. Magnus Manske chosen
    Magnus Manske is a German software developer and biochemist best known for creating the original version of the MediaWiki software that powers Wikipedia.
  • B. Magnus Poulsson
    Magnus Poulsson was a prominent Norwegian architect known for his traditionalist designs and major public buildings in the early 20th century.
  • C. Magnus Volk
    Magnus Volk was a British electrical engineer and inventor best known for pioneering electric railways, including creating one of the world’s oldest operating electric railways in Brighton.
  • D. Markus Rygaard
    Markus Rygaard is a Danish actor best known for his leading role as a troubled schoolboy in the Oscar-winning film "In a Better World."
  • E. Mads Tofte
    Mads Tofte is a Danish computer scientist known for his influential work on the design and implementation of the Standard ML programming language and its type system.
  • 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_69d8e51b014081908b263e167370529a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e653c659088190928fa4c9264135d3 completed April 20, 2026, 4:26 p.m.
Created at: April 10, 2026, 1:49 p.m.