Triple

T7302921
Position Surface form Disambiguated ID Type / Status
Subject Wachtberg E167902 entity
Predicate hasSubdivision P747 FINISHED
Object Arzdorf
Arzdorf is a village and district of the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
E654897 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: Arzdorf | Statement: [Wachtberg, hasSubdivision, Arzdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Arzdorf
Context triple: [Wachtberg, hasSubdivision, Arzdorf]
  • A. Urdorf
    Urdorf is a municipality in the canton of Zurich in Switzerland, located in the Limmat Valley near the city of Zurich.
  • B. Altdorf
    Altdorf is a Swiss town in the canton of Uri, known as a historic transit point through the Alps and its association with the William Tell legend.
  • C. Drensteinfurt
    Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
  • D. Eulachstadt
    Eulachstadt is a nickname for the Swiss city of Winterthur, reflecting its historical association with the Eulach River and its development as an important industrial and cultural center.
  • E. Niendorf
    Niendorf is a residential district in the northwestern part of Hamburg, Germany, known for its suburban character and proximity to Hamburg Airport.
  • 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: Arzdorf
Triple: [Wachtberg, hasSubdivision, Arzdorf]
Generated description
Arzdorf is a village and district of the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Arzdorf
Target entity description: Arzdorf is a village and district of the municipality of Wachtberg in the Rhein-Sieg-Kreis region of North Rhine-Westphalia, Germany.
  • A. Urdorf
    Urdorf is a municipality in the canton of Zurich in Switzerland, located in the Limmat Valley near the city of Zurich.
  • B. Altdorf
    Altdorf is a Swiss town in the canton of Uri, known as a historic transit point through the Alps and its association with the William Tell legend.
  • C. Drensteinfurt
    Drensteinfurt is a small town in North Rhine-Westphalia, Germany, known for its historic architecture and location in the Münsterland region.
  • D. Eulachstadt
    Eulachstadt is a nickname for the Swiss city of Winterthur, reflecting its historical association with the Eulach River and its development as an important industrial and cultural center.
  • E. Niendorf
    Niendorf is a residential district in the northwestern part of Hamburg, Germany, known for its suburban character and proximity to Hamburg Airport.
  • 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_69c6888c820881909fc68f689fe1c251 completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6ebb2261c8190ae9095c8e110b528 completed March 27, 2026, 8:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7e558098c819091562566c59332e2 completed March 28, 2026, 2:27 p.m.
NEDg Description generation batch_69c7e6671e2c8190aed42aa673540efa completed March 28, 2026, 2:32 p.m.
NED2 Entity disambiguation (via description) batch_69c7e6cd820881909ef8fd3bc28d2716 completed March 28, 2026, 2:33 p.m.
Created at: March 27, 2026, 3:01 p.m.