Triple

T724303
Position Surface form Disambiguated ID Type / Status
Subject Michaelis-Kirchweih E14689 entity
Predicate locatedIn P40 FINISHED
Object Franken
Franken is a culturally rich region in northern Bavaria, Germany, known for its historic towns, traditional festivals, and distinctive Franconian wine and beer.
 E88779 NE FINISHED

How this triple was built (4 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: Franken | Statement: [Michaelis-Kirchweih, locatedIn, Franken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Franken
Context triple: [Michaelis-Kirchweih, locatedIn, Franken]
  • A. Freddy
    Freddy is a common diminutive or nickname for the given name Alfred.
  • B. Victor
    Victor is a masculine given name of Latin origin meaning "conqueror" or "winner," commonly used in many European and English-speaking countries.
  • C. Abraham Franklin Frankenstein
    Abraham Franklin Frankenstein was an American composer best known for writing the music to the state song of California, "I Love You, California."
  • D. Monster
    Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
  • E. Monster
    Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Franken
Triple: [Michaelis-Kirchweih, locatedIn, Franken]
Generated description
Franken is a culturally rich region in northern Bavaria, Germany, known for its historic towns, traditional festivals, and distinctive Franconian wine and beer.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Franken
Target entity description: Franken is a culturally rich region in northern Bavaria, Germany, known for its historic towns, traditional festivals, and distinctive Franconian wine and beer.
  • A. Freddy
    Freddy is a common diminutive or nickname for the given name Alfred.
  • B. Victor
    Victor is a masculine given name of Latin origin meaning "conqueror" or "winner," commonly used in many European and English-speaking countries.
  • C. Abraham Franklin Frankenstein
    Abraham Franklin Frankenstein was an American composer best known for writing the music to the state song of California, "I Love You, California."
  • D. Monster
    Monster is a town in the Dutch province of South Holland, known for its coastal location near the North Sea and its greenhouse horticulture.
  • E. Monster
    Monster is a 2003 biographical crime drama film in which Charlize Theron delivers an Oscar-winning performance as serial killer Aileen Wuornos.
  • F. None of above. chosen

Provenance (5 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_69a4934c753c81909b309027e48b9b3a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5a6ab508190b70a05a9d77829a5 completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a654de91bc819088d495c8a9cb4133 completed March 3, 2026, 3:26 a.m.
NEDg Description generation batch_69a6570367048190835a8cc857ea2773 completed March 3, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_69a6575696588190b59fc63ebf5f324a completed March 3, 2026, 3:36 a.m.
Created at: March 1, 2026, 7:37 p.m.