Triple

T17851288
Position Surface form Disambiguated ID Type / Status
Subject Karl Moser E445811 entity
Predicate name P16 FINISHED
Object Karl Moser 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: Karl Moser | Statement: [Karl Moser, name, Karl Moser]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karl Moser
Context triple: [Karl Moser, name, Karl Moser]
  • A. Karl Moser chosen
    Karl Moser was a prominent Swiss architect and educator known for his influential role in early modern architecture and his leadership within the architectural profession in Switzerland.
  • B. Karl Senger
    Karl Senger is a relatively obscure individual known primarily for sharing the surname Senger, with no widely documented public achievements or biographical details.
  • C. Walter Naegle
    Walter Naegle is an American activist and archivist best known as the longtime partner and estate executor of civil rights leader Bayard Rustin.
  • D. Karl Kling
    Karl Kling was a German racing driver of the 1950s who competed for the Mercedes-Benz works team in Grand Prix and sports car events.
  • E. Walter Wolf
    Walter Wolf is a Slovenian-Canadian businessman best known for his involvement in Formula One through the Wolf Racing team and for his ventures in the oil and tobacco industries.
  • 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_69d8b9f26f18819089c9e43250bee6ae completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48fff6c288190a2b5e60b66c03ddc completed April 19, 2026, 8:19 a.m.
Created at: April 10, 2026, 10:17 a.m.