Triple

T12502007
Position Surface form Disambiguated ID Type / Status
Subject Eduard Dietl E298849 entity
Predicate givenName P17 FINISHED
Object Eduard E830385 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: Eduard | Statement: [Eduard Dietl, givenName, Eduard]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eduard
Context triple: [Eduard Dietl, givenName, Eduard]
  • A. Eduard
    Eduard was the younger son of physicist Albert Einstein, known for his promising studies in psychiatry and his lifelong struggle with schizophrenia.
  • B. Eduard
    Eduard is one of the central protagonists in Johann Wolfgang von Goethe’s novel "Elective Affinities," whose actions and relationships drive the story’s exploration of passion, marriage, and moral conflict.
  • C. Eduard
    Eduard "Del" Delacroix is a fictional death row inmate from Stephen King's novel "The Green Mile," known for his close bond with a pet mouse and his tragic execution.
  • D. Eduard chosen
    Eduard is a masculine given name of German origin, commonly used in various European countries.
  • E. Eduard
    Eduard is a central character in Paulo Coelho’s novel "Veronika Decides to Die," portrayed as a sensitive, introspective young man whose relationship with the protagonist profoundly influences her view of life and death.
  • 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_69d6ada4cd388190ae3bbf83ff87057a completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94dfbb2a48190a231b02cfa990565 completed April 10, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75464d85c8190a4c27f22cfd7dc96 completed May 3, 2026, 1:57 p.m.
Created at: April 8, 2026, 9:57 p.m.