Triple

T8922071
Position Surface form Disambiguated ID Type / Status
Subject Geltendorf E212443 entity
Predicate locatedNear P294 FINISHED
Object city of Munich E21335 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: city of Munich | Statement: [Geltendorf, locatedNear, city of Munich]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: city of Munich
Context triple: [Geltendorf, locatedNear, city of Munich]
  • A. Stadt Nürnberg
    Stadt Nürnberg is the municipal government of the German city of Nuremberg, responsible for local administration, public services, and urban infrastructure.
  • B. Munich chosen
    Munich is the capital and largest city of the German state of Bavaria, renowned for its rich cultural scene, historic architecture, and the annual Oktoberfest beer festival.
  • C. City of Berlin
    The City of Berlin is the capital and largest city of Germany, known for its pivotal role in European history, vibrant cultural scene, and status as a major political, economic, and artistic center.
  • D. Munich metropolitan region
    The Munich metropolitan region is a major economic and cultural hub in southern Germany centered around the city of Munich and encompassing numerous surrounding towns and districts.
  • E. 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.
  • 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_69ca839481d48190b42b037e0d0f636c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc665143688190872c681f4299bd9f completed April 1, 2026, 12:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfd091f3448190aadd847d117bc166 completed April 3, 2026, 2:37 p.m.
Created at: March 30, 2026, 6:56 p.m.