Triple

T12566891
Position Surface form Disambiguated ID Type / Status
Subject Province of Westphalia E295497 entity
Predicate containsSettlement P847 FINISHED
Object Bönen
Bönen is a small German town in the state of North Rhine-Westphalia, situated in the Ruhr area between Dortmund and Hamm.
 E990677 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: Bönen | Statement: [Province of Westphalia, containsSettlement, Bönen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bönen
Context triple: [Province of Westphalia, containsSettlement, Bönen]
  • A. Lonstein
    Lonstein is a surname of likely Ashkenazi Jewish origin borne by various individuals and families.
  • B. Bönnsch
    Bönnsch is a regional German beer style and dialect variant from Bonn, closely associated with and similar to the Kölsch tradition of nearby Cologne.
  • C. Borken
    Borken is a town in western Germany that serves as an administrative and commercial center in the state of North Rhine-Westphalia.
  • D. Borghorst
    Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
  • E. Brannenburg
    Brannenburg is a Bavarian municipality in southern Germany, known for its scenic Alpine setting and outdoor recreation opportunities.
  • 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: Bönen
Triple: [Province of Westphalia, containsSettlement, Bönen]
Generated description
Bönen is a small German town in the state of North Rhine-Westphalia, situated in the Ruhr area between Dortmund and Hamm.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bönen
Target entity description: Bönen is a small German town in the state of North Rhine-Westphalia, situated in the Ruhr area between Dortmund and Hamm.
  • A. Lonstein
    Lonstein is a surname of likely Ashkenazi Jewish origin borne by various individuals and families.
  • B. Bönnsch
    Bönnsch is a regional German beer style and dialect variant from Bonn, closely associated with and similar to the Kölsch tradition of nearby Cologne.
  • C. Borken
    Borken is a town in western Germany that serves as an administrative and commercial center in the state of North Rhine-Westphalia.
  • D. Borghorst
    Borghorst is a district of the German town Steinfurt in North Rhine-Westphalia, known historically for its textile industry and regional cultural heritage.
  • E. Brannenburg
    Brannenburg is a Bavarian municipality in southern Germany, known for its scenic Alpine setting and outdoor recreation opportunities.
  • 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_69d6ad9cac2c81908e8a7bed82d1e21d completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d954a325948190994bcfc9d571a3a8 completed April 10, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f655914f908190afbebbec3cb57e73 completed May 2, 2026, 7:50 p.m.
NEDg Description generation batch_69f657e504c881909b960acc7758b39d completed May 2, 2026, 8 p.m.
NED2 Entity disambiguation (via description) batch_69f658a80fd08190b1b8c161ca6e56ec completed May 2, 2026, 8:03 p.m.
Created at: April 8, 2026, 11:49 p.m.