Triple

T11787257
Position Surface form Disambiguated ID Type / Status
Subject Mafalda of Savoy E280301 entity
Predicate placeOfDeath P21 FINISHED
Object Weimar, Thuringia E111310 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: Weimar, Thuringia | Statement: [Mafalda of Savoy, placeOfDeath, Weimar, Thuringia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Weimar, Thuringia
Context triple: [Mafalda of Savoy, placeOfDeath, Weimar, Thuringia]
  • A. Weimar chosen
    Weimar is a historic German city renowned as a center of culture and the arts, associated with figures like Goethe and Schiller and pivotal movements in modern design and architecture.
  • B. Weimar (Lahn)
    Weimar (Lahn) is a small municipality in the German state of Hesse, located near the university city of Marburg.
  • C. Friedberg, Hesse
    Friedberg, Hesse is a historic town in central Germany’s state of Hesse, known for its medieval architecture and prominent hilltop fortress.
  • D. Erfurt
    Erfurt is a historic German city in the state of Thuringia, known for its well-preserved medieval old town and as an important cultural and educational center.
  • E. Hesse, Germany
    Hesse, Germany is a federal state in central Germany known for its financial hub Frankfurt am Main, forested landscapes, and historic cities such as Wiesbaden and Kassel.
  • 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a586803481909af0032c35ca6e51 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f090e8828481908baa7f6067190db3 completed April 28, 2026, 10:50 a.m.
Created at: April 8, 2026, 9:42 p.m.