Triple

T13469040
Position Surface form Disambiguated ID Type / Status
Subject Essen E311580 entity
Predicate hasDistrict P459 FINISHED
Object Werden
Werden is a historic district of the German city of Essen, known for its old town charm and the former Benedictine abbey that now houses the Folkwang University of the Arts.
 E1042727 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: Werden | Statement: [Essen, hasDistrict, Werden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Werden
Context triple: [Essen, hasDistrict, Werden]
  • A. Worden
    Worden is the surname of Alfred M. Worden, the American astronaut who served as the command module pilot for NASA's Apollo 15 mission.
  • B. Werdet
    Werdet was a 19th-century French publisher known for issuing works by prominent authors such as Honoré de Balzac.
  • C. Welver
    Welver is a municipality in the German state of North Rhine-Westphalia, situated in the Soest district within the historic region of Westphalia.
  • D. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • E. Weenen
    Weenen is a small historic town in KwaZulu-Natal, South Africa, known for its agricultural surroundings and proximity to the Weenen Game Reserve.
  • 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: Werden
Triple: [Essen, hasDistrict, Werden]
Generated description
Werden is a historic district of the German city of Essen, known for its old town charm and the former Benedictine abbey that now houses the Folkwang University of the Arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Werden
Target entity description: Werden is a historic district of the German city of Essen, known for its old town charm and the former Benedictine abbey that now houses the Folkwang University of the Arts.
  • A. Worden
    Worden is the surname of Alfred M. Worden, the American astronaut who served as the command module pilot for NASA's Apollo 15 mission.
  • B. Werdet
    Werdet was a 19th-century French publisher known for issuing works by prominent authors such as Honoré de Balzac.
  • C. Welver
    Welver is a municipality in the German state of North Rhine-Westphalia, situated in the Soest district within the historic region of Westphalia.
  • D. Gweru
    Gweru is a central Zimbabwean city that serves as the capital of the Midlands Province and an important commercial and transportation hub.
  • E. Weenen
    Weenen is a small historic town in KwaZulu-Natal, South Africa, known for its agricultural surroundings and proximity to the Weenen Game Reserve.
  • 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_69d806a938b8819097ec43a2229fc7f9 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf21e46081908a00c9acf54f270f completed April 12, 2026, 2:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69f74629f1408190b54194fe794be39a completed May 3, 2026, 12:57 p.m.
NEDg Description generation batch_69f7493168a481908f99283914b39db5 completed May 3, 2026, 1:10 p.m.
NED2 Entity disambiguation (via description) batch_69f7499aa5ac8190a93afda115694795 completed May 3, 2026, 1:11 p.m.
Created at: April 9, 2026, 9:42 p.m.