Triple

T6059676
Position Surface form Disambiguated ID Type / Status
Subject Karlsruhe Palace E135002 entity
Predicate namedAfter P63 FINISHED
Object Karlsruhe E24580 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: Karlsruhe | Statement: [Karlsruhe Palace, namedAfter, Karlsruhe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karlsruhe
Context triple: [Karlsruhe Palace, namedAfter, Karlsruhe]
  • A. Karlsruhe chosen
    Karlsruhe is a major city in southwestern Germany best known as the seat of the country’s highest courts and a central hub of German constitutional jurisprudence.
  • B. Stuttgart
    Stuttgart is a major city in southwestern Germany known as an important industrial, cultural, and economic center, particularly famous for its automotive industry and surrounding wine-growing region.
  • C. Mannheim
    Mannheim is a major city in southwestern Germany, known as an important industrial, commercial, and cultural center at the confluence of the Rhine and Neckar rivers.
  • D. Darmstadt
    Darmstadt is a city in the German state of Hesse known for its historical ties to the Grand Duchy of Hesse and its role as a center of science, technology, and Art Nouveau culture.
  • E. Heilbronn
    Heilbronn is a city in the German state of Baden-Württemberg known for its industrial base, wine production, and role as a regional economic and educational hub.
  • 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_69c00878d06881909ee78e88913bf890 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c0571e479c8190bec0e1439b4cf68f completed March 22, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c988df1e4c8190a46c971a3f9b2d49 completed March 29, 2026, 8:17 p.m.
Created at: March 22, 2026, 4:10 p.m.