Triple

T2485507
Position Surface form Disambiguated ID Type / Status
Subject Johannes Agricola E55916 entity
Predicate birthPlace P1 FINISHED
Object Eisleben E58677 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: Eisleben | Statement: [Johannes Agricola, birthPlace, Eisleben]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eisleben
Context triple: [Johannes Agricola, birthPlace, Eisleben]
  • A. Eisleben chosen
    Eisleben is a historic town in the German state of Saxony-Anhalt, best known as the birthplace of Protestant Reformer Martin Luther.
  • B. 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.
  • C. Wernigerode
    Wernigerode is a picturesque German town in Saxony-Anhalt known for its colorful half-timbered houses, medieval castle, and location on the northern slopes of the Harz Mountains.
  • D. Naumburg
    Naumburg is a historic town in the German state of Saxony-Anhalt, known for its medieval cathedral and as the childhood home of philosopher Friedrich Nietzsche.
  • E. Wittenberg
    Wittenberg is a historic German city best known as the cradle of the Protestant Reformation and the place where Martin Luther taught and preached.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd175f5ac8190870db9c6cb8e45bd completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17ba433481908eccda5c6c6246be completed March 9, 2026, 6:55 p.m.
Created at: March 6, 2026, 9:45 p.m.