Triple

T10988203
Position Surface form Disambiguated ID Type / Status
Subject Main-Spessart E259685 entity
Predicate capital P234 FINISHED
Object Karlstadt am Main E832497 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: Karlstadt am Main | Statement: [Main-Spessart, capital, Karlstadt am Main]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Karlstadt am Main
Context triple: [Main-Spessart, capital, Karlstadt am Main]
  • A. Karlstadt am Main chosen
    Karlstadt am Main is a historic town in northern Bavaria, Germany, situated on the River Main and known for its medieval old town and surrounding wine-growing region.
  • B. Saalfeld
    Saalfeld is a town in the German state of Thuringia, known for its historic old town and former significance as a regional railway and industrial center.
  • C. Schmalkalden
    Schmalkalden is a historic town in the German state of Thuringia, known for its well-preserved medieval architecture and role in Reformation-era politics.
  • D. Eisenach
    Eisenach is a historic town in central Germany best known for its associations with Martin Luther and as the birthplace of composer Johann Sebastian Bach.
  • E. Mühlhausen
    Mühlhausen is a historic town in central Germany, known for its well-preserved medieval architecture and cultural heritage.
  • 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_69d6aa8a6a548190a750f944ccdc8064 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d787b574d08190adec34b814a26437 completed April 9, 2026, 11:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69e344f95ab88190bbce8f0eab0b2713 completed April 18, 2026, 8:46 a.m.
Created at: April 8, 2026, 9:24 p.m.