Triple

T1983900
Position Surface form Disambiguated ID Type / Status
Subject Grand Duchy of Saxe-Weimar-Eisenach E43093 entity
Predicate capital P234 FINISHED
Object Eisenach E153084 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: Eisenach | Statement: [Grand Duchy of Saxe-Weimar-Eisenach, capital, Eisenach]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eisenach
Context triple: [Grand Duchy of Saxe-Weimar-Eisenach, capital, Eisenach]
  • A. Eisenach chosen
    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.
  • B. Gotha
    Gotha is a historic German city in Thuringia known for its former ducal court, cultural heritage, and role as a residence of various German noble houses.
  • C. Altenburg
    Altenburg is a historic town in eastern Thuringia, Germany, known for its playing-card tradition and as the birthplace of the card game Skat.
  • D. Erfurt
    Erfurt is a historic German city in the state of Thuringia, known for its well-preserved medieval old town and as an important cultural and educational center.
  • E. 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.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb820815481908aac6d89b437225b completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69b2f39ad7488190a7604a113c2e2bb3 completed March 12, 2026, 5:10 p.m.
Created at: March 4, 2026, 7:37 p.m.