Triple

T20324416
Position Surface form Disambiguated ID Type / Status
Subject Arnulf Klett E492293 entity
Predicate employer P7 FINISHED
Object City of Stuttgart 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: City of Stuttgart | Statement: [Arnulf Klett, employer, City of Stuttgart]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Stuttgart
Context triple: [Arnulf Klett, employer, City of Stuttgart]
  • A. Stuttgart chosen
    Stuttgart is a major city in southwestern Germany known as an important industrial, cultural, and economic center, particularly famous for its automotive industry and surrounding wine-growing region.
  • B. Stadt Nürnberg
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • C. Ingolstadt
    Ingolstadt is a historic city in southern Germany known for its medieval architecture, university tradition, and role as a major hub of the automotive industry.
  • D. Stadt Fürth
    Stadt Fürth is a Bavarian city in Germany known for its rich Franconian cultural traditions, historic architecture, and vibrant local festivals.
  • E. Karlsruhe
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • 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_69e0b4a0134081909113563e1c3ba68a completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6778e59508190bfd7a3ce44d56a93 completed April 20, 2026, 6:59 p.m.
Created at: April 16, 2026, 11:21 a.m.