Triple

T11029413
Position Surface form Disambiguated ID Type / Status
Subject Hessian E260718 entity
Predicate hasDialect P4251 FINISHED
Object Frankfurt dialect E21394 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 dialect | Statement: [Hessian, hasDialect, Frankfurt dialect]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frankfurt dialect
Context triple: [Hessian, hasDialect, Frankfurt dialect]
  • A. Nuremberg dialect
    The Nuremberg dialect is a regional variety of East Franconian German traditionally spoken in and around the city of Nuremberg in Bavaria.
  • B. Rhenish Franconian chosen
    Rhenish Franconian is a group of West Central German dialects spoken primarily in parts of western Germany, Luxembourg, and eastern France.
  • C. South Franconian German
    South Franconian German is a regional Upper German dialect spoken primarily in parts of southwestern Germany, notably around the northern Baden-Württemberg area.
  • D. Bamberg dialect
    The Bamberg dialect is a regional variety of the East Franconian German dialect spoken in and around the city of Bamberg in northern Bavaria.
  • E. Kölsch dialect
    The Kölsch dialect is a regional variety of the German language spoken in and around Cologne, known for its distinct pronunciation, vocabulary, and strong cultural identity.
  • 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_69d6aa979bdc8190bf0e79104cc098c1 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d797d2feb881909a5684721e8b0d9c completed April 9, 2026, 12:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3a997d7bc8190982467039e0f5504 completed April 18, 2026, 3:56 p.m.
Created at: April 8, 2026, 9:25 p.m.