Triple

T20667621
Position Surface form Disambiguated ID Type / Status
Subject Battle of Stadtlohn E507933 entity
Predicate location P40 FINISHED
Object Stadtlohn, Westphalia 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: Stadtlohn, Westphalia | Statement: [Battle of Stadtlohn, location, Stadtlohn, Westphalia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Stadtlohn, Westphalia
Context triple: [Battle of Stadtlohn, location, Stadtlohn, Westphalia]
  • A. Stadtlohn chosen
    Stadtlohn is a small town in western Germany’s Münsterland region, near the Dutch border, known for its rural character and local industry.
  • B. Iserlohn
    Iserlohn is a city in the Märkischer Kreis district of North Rhine-Westphalia, Germany, known historically for its role in World War II and its metalworking and industrial heritage.
  • C. Lüdinghausen
    Lüdinghausen is a historic town in western Germany known for its medieval castles and picturesque setting in the Münsterland region.
  • D. Lüdenscheid
    Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
  • E. Gütersloh
    Gütersloh is a city in the German state of North Rhine-Westphalia known for being the headquarters of major companies like Bertelsmann and Miele.
  • 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_69e0b4c059bc81908ea762cd73ea4424 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6b5c4c4608190ae17da4a59e5ae80 completed April 20, 2026, 11:24 p.m.
Created at: April 16, 2026, 11:44 a.m.