Triple

T1839268
Position Surface form Disambiguated ID Type / Status
Subject Minister-President of Bavaria E41135 entity
Predicate genderOfTitle P1805 FINISHED
Object masculine form LITERAL 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: masculine form | Statement: [Minister-President of Bavaria, genderOfTitle, masculine form]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: genderOfTitle
Context triple: [Minister-President of Bavaria, genderOfTitle, masculine form]
  • A. hasGenderedTitle chosen
    Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
  • B. genderRule
    Indicates a rule or constraint that determines how gender-related properties or classifications should be assigned or interpreted in a given context.
  • C. genderDivision
    Indicates a relationship where roles, responsibilities, or categories are separated or distinguished based on gender.
  • D. namedForGender
    Indicates that one entity is named in a way that reflects or is derived from a particular gender or gender-related characteristic of another entity.
  • E. genderUsage
    Indicates how a particular gender is applied, referenced, or treated within a given context or system.
  • F. None of above.

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_69a88647f9388190909bc36e795bdaec completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb32d35508190bf1c487dffbecaf0 completed March 7, 2026, 5:10 a.m.
PD Predicate disambiguation batch_69abafd88ebc81908208394746351fe6 completed March 7, 2026, 4:55 a.m.
Created at: March 4, 2026, 7:33 p.m.