Triple

T1001491
Position Surface form Disambiguated ID Type / Status
Subject Jeppesen E21611 entity
Predicate hasOfficeIn P1268 FINISHED
Object Frankfurt, Germany E16481 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: Frankfurt, Germany | Statement: [Jeppesen, hasOfficeIn, Frankfurt, Germany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt, Germany
Context triple: [Jeppesen, hasOfficeIn, Frankfurt, Germany]
  • A. Frankfurt am Main chosen
    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.
  • B. Wiesbaden
    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.
  • 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. Bonn
    Bonn is a historic German city on the Rhine River, best known for being the birthplace of Ludwig van Beethoven and the former seat of the federal government before reunification.
  • E. 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.
  • 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_69a493c53e648190ae8cb76c433fd9a7 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b4fcbc04819098d2125518f62ae7 completed March 1, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69afe849ea708190be6393a21fd6da33 completed March 10, 2026, 9:45 a.m.
Created at: March 1, 2026, 7:41 p.m.