Triple

T17102573
Position Surface form Disambiguated ID Type / Status
Subject Lünen E415014 entity
Predicate hasLandmark P105 FINISHED
Object Lünen Town Hall E337295 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: Lünen Town Hall | Statement: [Lünen, hasLandmark, Lünen Town Hall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lünen Town Hall
Context triple: [Lünen, hasLandmark, Lünen Town Hall]
  • A. Lünen Town Hall chosen
    Lünen Town Hall is the central municipal building and administrative seat of the city of Lünen in North Rhine-Westphalia, Germany.
  • B. Lüneburg Town Hall
    Lüneburg Town Hall is a historic and architecturally significant city hall in Lüneburg, Germany, renowned for its medieval and baroque elements.
  • C. Leonberg town hall
    Leonberg town hall is the main municipal administrative building and local government seat of the town of Leonberg in Baden-Württemberg, Germany.
  • D. Heilbronn Town Hall
    Heilbronn Town Hall is a historic Renaissance building in Heilbronn, Germany, best known for its ornate astronomical clock and richly decorated façade.
  • E. Kaiserslautern Town Hall
    Kaiserslautern Town Hall is a prominent modernist high-rise administrative building and architectural landmark in the city of Kaiserslautern, Germany.
  • 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_69d886cfc8e88190b05ba466edd35591 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3dc2495c88190b5b16a006a994faf completed April 18, 2026, 7:31 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0139ffbe808190a24e827331ee4a6c completed May 11, 2026, 2:07 a.m.
Created at: April 10, 2026, 5:35 a.m.