Triple

T1843704
Position Surface form Disambiguated ID Type / Status
Subject County of Nassau-Beilstein E41235 entity
Predicate capital P234 FINISHED
Object Beilstein
Beilstein is a small historic town in western Germany, known for its medieval architecture and picturesque setting in the Lahn region.
 E206035 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: Beilstein | Statement: [County of Nassau-Beilstein, capital, Beilstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Beilstein
Context triple: [County of Nassau-Beilstein, capital, Beilstein]
  • A. Diels–Kranz collection
    The Diels–Kranz collection is the standard scholarly compilation and numbering system of the surviving fragments and testimonia of the Presocratic philosophers.
  • B. Elster
    Elster is a river in central Europe, primarily flowing through the German state of Saxony and its surrounding regions.
  • C. Houben
    Houben is a Dutch surname borne by various notable individuals, including architect Francine Houben.
  • D. Gütermann
    Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
  • E. Albertinum
    The Albertinum is a renowned art museum in Dresden, Germany, best known for its extensive collections of modern art and sculpture.
  • 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: Beilstein
Triple: [County of Nassau-Beilstein, capital, Beilstein]
Generated description
Beilstein is a small historic town in western Germany, known for its medieval architecture and picturesque setting in the Lahn region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Beilstein
Target entity description: Beilstein is a small historic town in western Germany, known for its medieval architecture and picturesque setting in the Lahn region.
  • A. Diels–Kranz collection
    The Diels–Kranz collection is the standard scholarly compilation and numbering system of the surviving fragments and testimonia of the Presocratic philosophers.
  • B. Elster
    Elster is a river in central Europe, primarily flowing through the German state of Saxony and its surrounding regions.
  • C. Houben
    Houben is a Dutch surname borne by various notable individuals, including architect Francine Houben.
  • D. Gütermann
    Gütermann is a German surname most notably associated with the Gütermann family involved in industry and manufacturing, particularly in the production of sewing threads.
  • E. Albertinum
    The Albertinum is a renowned art museum in Dresden, Germany, best known for its extensive collections of modern art and sculpture.
  • 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_69a88648cd44819093303206d96d76ad completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb04eb0748190b226f932e544925f completed March 7, 2026, 4:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9be1ef481909cd6f6975bf2165d completed March 8, 2026, 7:10 p.m.
NEDg Description generation batch_69adcaf1917c819090eac27de62494ca completed March 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_69adcbba64588190aa0ebd2b6f67afa7 completed March 8, 2026, 7:19 p.m.
Created at: March 4, 2026, 7:33 p.m.