Triple

T2853117
Position Surface form Disambiguated ID Type / Status
Subject Britta Ernst E63136 entity
Predicate workLocation P7 FINISHED
Object Schleswig-Holstein E45540 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: Schleswig-Holstein | Statement: [Britta Ernst, workLocation, Schleswig-Holstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schleswig-Holstein
Context triple: [Britta Ernst, workLocation, Schleswig-Holstein]
  • A. Schleswig-Holstein chosen
    Schleswig-Holstein is Germany’s northernmost state, known for its North Sea and Baltic Sea coastlines, maritime heritage, and shared border with Denmark.
  • B. Mecklenburg-Vorpommern
    Mecklenburg-Vorpommern is a federal state in northeastern Germany known for its Baltic Sea coastline, numerous lakes, and relatively low population density.
  • C. Schleswig
    Schleswig is a historic town in northern Germany known for its Viking heritage, medieval cathedral, and location on the Schlei inlet.
  • D. Lower Saxony
    Lower Saxony is a large federal state in northwestern Germany known for its diverse landscapes, strong industrial base, and historic cities such as Hanover and Göttingen.
  • E. Brandenburg
    Brandenburg is a federal state in northeastern Germany that surrounds Berlin and is known for its lakes, forests, and historic Prussian heritage.
  • 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_69ab4c407c408190857d25e027155ce9 completed March 6, 2026, 9:50 p.m.
NER Named-entity recognition batch_69abdf5e043c8190ac82112abce7262a completed March 7, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69bf10a00c5c819090fa26ce068033b6 completed March 21, 2026, 9:41 p.m.
Created at: March 6, 2026, 10:02 p.m.