Triple

T16271337
Position Surface form Disambiguated ID Type / Status
Subject Kaiser-Friedrich-Museum E395005 entity
Predicate district P2709 FINISHED
Object Mitte E28609 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: Mitte | Statement: [Kaiser-Friedrich-Museum, district, Mitte]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mitte
Context triple: [Kaiser-Friedrich-Museum, district, Mitte]
  • A. Mitte chosen
    Mitte is the central district of Berlin, Germany, known as the historic core of the city and home to many major landmarks and government institutions.
  • B. Mitte
    Mitte is the central urban district of Ludwigshafen am Rhein, Germany, encompassing the city’s core commercial and administrative areas.
  • C. Mitte
    Mitte is the central urban district of Saarbrücken, Germany, encompassing much of the city’s administrative, commercial, and cultural core.
  • D. Mitte
    Mitte is a central urban district of the German city of Koblenz, encompassing key administrative, commercial, and historic areas.
  • E. Mide
    Mide was a medieval Irish kingdom located in the central part of Ireland, historically associated with the High Kings and the ruling dynasty of the Uí Néill.
  • 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_69d87f221d8081909b0b2063e7528ba2 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e246099dd081908e268a1a0cf8a373 completed April 17, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0017bf09888190b3d90db3517a2f1e completed May 10, 2026, 5:29 a.m.
Created at: April 10, 2026, 5:05 a.m.