Triple

T4671526
Position Surface form Disambiguated ID Type / Status
Subject Southwestern Germany E102972 entity
Predicate contains P35 FINISHED
Object Saarbrücken E269297 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: Saarbrücken | Statement: [Southwestern Germany, contains, Saarbrücken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saarbrücken
Context triple: [Southwestern Germany, contains, Saarbrücken]
  • A. Saarbrücken chosen
    Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
  • B. Saarlouis
    Saarlouis is a town in the German state of Saarland, known historically as a fortified city founded by Louis XIV of France near the French border.
  • C. Wissembourg
    Wissembourg is a historic town in northeastern France’s Alsace region, known for its well-preserved medieval architecture and proximity to the German border.
  • D. Kaiserslautern
    Kaiserslautern is a city in southwestern Germany known for its historic old town, technical university, and prominent football club 1. FC Kaiserslautern.
  • E. Lörrach
    Lörrach is a town in southwest Germany’s Baden-Württemberg state, near the borders with Switzerland and France, known for its proximity to Basel and its role as a regional economic and cultural center.
  • 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_69bd43d9cba4819086c1ab1c2d9d2133 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd63506090819083ff8271adc5ef75 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c67c2b4f188190a78b591674040060 completed March 27, 2026, 12:46 p.m.
Created at: March 20, 2026, 1:15 p.m.