Triple

T2884024
Position Surface form Disambiguated ID Type / Status
Subject University of Wittenberg E59462 entity
Predicate hasNotableProfessor P13831 FINISHED
Object Martin Luther E8525 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: Martin Luther | Statement: [University of Wittenberg, hasNotableProfessor, Martin Luther]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martin Luther
Context triple: [University of Wittenberg, hasNotableProfessor, Martin Luther]
  • A. Martin Luther chosen
    Martin Luther was a 16th-century German theologian and key figure of the Protestant Reformation whose teachings challenged Catholic doctrine and reshaped Western Christianity.
  • B. Martin Franz Luther
    Martin Franz Luther was a German Nazi diplomat and SS official who served in the Foreign Office and participated in the administration of the Holocaust.
  • C. Luther
    Luther is a masculine given name of Germanic origin, most famously borne by civil rights leader Martin Luther King Jr. and R&B singer Luther Vandross.
  • D. Luther
    Luther is a British psychological crime drama television series starring Idris Elba as a brilliant but troubled detective.
  • E. Luther
    Luther is a common German surname most famously associated with the Protestant Reformer Martin Luther and his family.
  • 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_69ab4ac739188190a112f42a5a69c951 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abe02e0ec48190b969ed921d179560 completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc5553e08190b9a49f23bb29fcc4 completed March 11, 2026, 5:23 a.m.
Created at: March 6, 2026, 10:03 p.m.