Triple

T802501
Position Surface form Disambiguated ID Type / Status
Subject Wrocław E17157 entity
Predicate twinCity P1072 FINISHED
Object Wiesbaden E254546 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: Wiesbaden | Statement: [Wrocław, twinCity, Wiesbaden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wiesbaden
Context triple: [Wrocław, twinCity, Wiesbaden]
  • A. Wiesbaden chosen
    Wiesbaden is a historic spa city in western Germany known for its thermal springs, elegant architecture, and role as a regional administrative and cultural center.
  • B. Frankfurt am Main
    Frankfurt am Main is a major German financial and transportation hub on the River Main, known for hosting the European Central Bank and one of Europe’s busiest airports.
  • C. 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.
  • D. 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.
  • E. Offenbach am Main
    Offenbach am Main is a city in central Germany located on the River Main, adjacent to Frankfurt, known historically for its leather industry and 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_69a49378b9c48190adbf5f62e5b7aca1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4aabd9fc081908ccadd8e8769de2d completed March 1, 2026, 9:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69af8309912c819096cf0dad7039ec95 completed March 10, 2026, 2:33 a.m.
Created at: March 1, 2026, 7:38 p.m.