Triple

T13617706
Position Surface form Disambiguated ID Type / Status
Subject Herschel Marx E325361 entity
Predicate residence P75 FINISHED
Object Saarlouis E339974 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: Saarlouis | Statement: [Herschel Marx, residence, Saarlouis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saarlouis
Context triple: [Herschel Marx, residence, Saarlouis]
  • A. Saarlouis chosen
    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.
  • B. Saarbrücken
    Saarbrücken is a German city on the Saar River known as an industrial, cultural, and educational center near the French border.
  • C. 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.
  • D. 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.
  • E. Wiehl
    Wiehl is a small town in western Germany’s North Rhine-Westphalia region, known for its picturesque setting in the hilly Bergisches Land and its mix of rural charm and light 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0ae77e0819081e3b14642460dc6 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd19259070819089bd3caf66e5af29 completed May 7, 2026, 10:58 p.m.
Created at: April 9, 2026, 9:50 p.m.