Triple

T12877833
Position Surface form Disambiguated ID Type / Status
Subject Leipzig metropolitan region E308012 entity
Predicate containsCity P294 FINISHED
Object Kayna
Kayna is a small town located within the Leipzig metropolitan region in eastern Germany.
 E1006474 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: Kayna | Statement: [Leipzig metropolitan region, containsCity, Kayna]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kayna
Context triple: [Leipzig metropolitan region, containsCity, Kayna]
  • A. Mayar
    Mayar is a mountain in the Grampian range of Angus, Scotland, popular with hikers and often climbed together with its neighboring peak Driesh.
  • B. Madison Davenport
    Madison Davenport is an American actress and singer known for her roles in film and television, including prominent performances in horror and drama series.
  • C. Serena
    Serena was a prominent noblewoman of the late Western Roman Empire, known as the influential wife of the powerful general Stilicho and a member of the imperial Theodosian dynasty.
  • D. Serena
    Serena is one of Elle Woods’ bubbly and supportive Delta Nu sorority sisters in the Broadway musical adaptation of "Legally Blonde."
  • E. Serena
    Serena is a central character in George Gershwin's opera "Porgy and Bess," known as a strong, devout woman who provides emotional and moral support within the Catfish Row community.
  • 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: Kayna
Triple: [Leipzig metropolitan region, containsCity, Kayna]
Generated description
Kayna is a small town located within the Leipzig metropolitan region in eastern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kayna
Target entity description: Kayna is a small town located within the Leipzig metropolitan region in eastern Germany.
  • A. Mayar
    Mayar is a mountain in the Grampian range of Angus, Scotland, popular with hikers and often climbed together with its neighboring peak Driesh.
  • B. Madison Davenport
    Madison Davenport is an American actress and singer known for her roles in film and television, including prominent performances in horror and drama series.
  • C. Serena
    Serena was a prominent noblewoman of the late Western Roman Empire, known as the influential wife of the powerful general Stilicho and a member of the imperial Theodosian dynasty.
  • D. Serena
    Serena is one of Elle Woods’ bubbly and supportive Delta Nu sorority sisters in the Broadway musical adaptation of "Legally Blonde."
  • E. Serena
    Serena is a central character in George Gershwin's opera "Porgy and Bess," known as a strong, devout woman who provides emotional and moral support within the Catfish Row community.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970fa8474819086a8af3c90f3ca84 completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69bb83bac8190838f7537b806317c completed May 3, 2026, 12:50 a.m.
NEDg Description generation batch_69f69cc6fa84819093a4317ab355f62b completed May 3, 2026, 12:54 a.m.
NED2 Entity disambiguation (via description) batch_69f69d845a9081909b40562825c1c500 completed May 3, 2026, 12:57 a.m.
Created at: April 9, 2026, 5:38 p.m.