Triple

T8838031
Position Surface form Disambiguated ID Type / Status
Subject Martin Luther University of Halle-Wittenberg E210315 entity
Predicate namedAfter P63 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: [Martin Luther University of Halle-Wittenberg, namedAfter, Martin Luther]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Martin Luther
Context triple: [Martin Luther University of Halle-Wittenberg, namedAfter, 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 common German surname most famously associated with the Protestant Reformer Martin Luther and his family.
  • E. Luther
    Luther is a central criminal-turned-vampire character in the horror film "From Dusk Till Dawn 2: Texas Blood Money."
  • 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_69ca8388549c819095fd94eadefbb007 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc606c60ac8190b2b6bd7f042c02f8 completed April 1, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf892b813481909739f72ffd080f49 completed April 3, 2026, 9:32 a.m.
Created at: March 30, 2026, 6:48 p.m.