Triple

T12566866
Position Surface form Disambiguated ID Type / Status
Subject Province of Westphalia E295497 entity
Predicate containsSettlement P847 FINISHED
Object Geseke
Geseke is a small town in western Germany located in the historical region of Westphalia.
 E990673 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: Geseke | Statement: [Province of Westphalia, containsSettlement, Geseke]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Geseke
Context triple: [Province of Westphalia, containsSettlement, Geseke]
  • A. Bothasig
    Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
  • B. Vryburg
    Vryburg is a large agricultural and commercial town in South Africa’s North West Province, historically known as a key cattle-farming and transport hub.
  • C. Witpoortjie
    Witpoortjie is a residential suburb in Roodepoort, South Africa, known for its proximity to the Witpoortjie Falls and the Walter Sisulu National Botanical Garden.
  • D. Tulbagh
    Tulbagh is a historic town in South Africa’s Western Cape, known for its Cape Dutch architecture and surrounding wine-producing valley.
  • E. Rustenburg
    Rustenburg is a city in South Africa’s North West Province known for its mining industry and as one of the venues for the 2010 FIFA World Cup.
  • 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: Geseke
Triple: [Province of Westphalia, containsSettlement, Geseke]
Generated description
Geseke is a small town in western Germany located in the historical region of Westphalia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Geseke
Target entity description: Geseke is a small town in western Germany located in the historical region of Westphalia.
  • A. Bothasig
    Bothasig is a residential suburb in the northern part of Cape Town, South Africa.
  • B. Vryburg
    Vryburg is a large agricultural and commercial town in South Africa’s North West Province, historically known as a key cattle-farming and transport hub.
  • C. Witpoortjie
    Witpoortjie is a residential suburb in Roodepoort, South Africa, known for its proximity to the Witpoortjie Falls and the Walter Sisulu National Botanical Garden.
  • D. Tulbagh
    Tulbagh is a historic town in South Africa’s Western Cape, known for its Cape Dutch architecture and surrounding wine-producing valley.
  • E. Rustenburg
    Rustenburg is a city in South Africa’s North West Province known for its mining industry and as one of the venues for the 2010 FIFA World Cup.
  • 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.