Triple

T7108786
Position Surface form Disambiguated ID Type / Status
Subject Christian Wulff E165657 entity
Predicate birthPlace P1 FINISHED
Object Osnabrück E22113 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: Osnabrück | Statement: [Christian Wulff, birthPlace, Osnabrück]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osnabrück
Context triple: [Christian Wulff, birthPlace, Osnabrück]
  • A. Osnabrück chosen
    Osnabrück is a historic city in Lower Saxony, Germany, known for its medieval architecture and role in the Peace of Westphalia.
  • B. Bielefeld
    Bielefeld is a major city in northwestern Germany known for its industrial heritage, university, and the tongue-in-cheek “Bielefeld conspiracy” meme claiming it does not exist.
  • C. Paderborn
    Paderborn is a historic city in western Germany known for its medieval cathedral, role as a regional religious and cultural center, and strategic importance during World War II.
  • D. Münster
    Münster is a historic city in western Germany known as one of the principal sites where the Peace of Westphalia treaties were negotiated and signed, ending the Thirty Years' War in 1648.
  • E. Braunschweig
    Braunschweig is a historic city in northern Germany known for its medieval architecture, cultural institutions, and role as an important economic and scientific 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_69c6888120f081908f8f01b201dc4a4c completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e5bcf3e08190bd8c6cf896c416c4 completed March 27, 2026, 8:17 p.m.
NED1 Entity disambiguation (via context triple) batch_69cc638e62608190958e90b07138a1cc completed April 1, 2026, 12:15 a.m.
Created at: March 27, 2026, 2:43 p.m.