Triple

T8337662
Position Surface form Disambiguated ID Type / Status
Subject Hattingen E195827 entity
Predicate hasSubdivision P747 FINISHED
Object Welper
Welper is a district of the town of Hattingen in North Rhine-Westphalia, Germany.
 E724681 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: Welper | Statement: [Hattingen, hasSubdivision, Welper]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Welper
Context triple: [Hattingen, hasSubdivision, Welper]
  • A. Weitzel
    Weitzel is a surname of likely German or Dutch origin borne by individuals such as Edu Weitzel Douwes Dekker.
  • B. Seelbach
    Seelbach is a municipality in southwestern Germany’s Baden-Württemberg region, situated in the Ortenau district near the Black Forest.
  • C. Eickeloh
    Eickeloh is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
  • D. Oberholzer
    Oberholzer is a surname of Germanic origin, commonly found in German-speaking regions and among their diasporas.
  • E. Beyenburg
    Beyenburg is a historic district in the eastern part of Wuppertal, Germany, known for its medieval monastery, reservoir, and well-preserved village character.
  • 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: Welper
Triple: [Hattingen, hasSubdivision, Welper]
Generated description
Welper is a district of the town of Hattingen in North Rhine-Westphalia, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Welper
Target entity description: Welper is a district of the town of Hattingen in North Rhine-Westphalia, Germany.
  • A. Weitzel
    Weitzel is a surname of likely German or Dutch origin borne by individuals such as Edu Weitzel Douwes Dekker.
  • B. Seelbach
    Seelbach is a municipality in southwestern Germany’s Baden-Württemberg region, situated in the Ortenau district near the Black Forest.
  • C. Eickeloh
    Eickeloh is a small municipality in Lower Saxony, Germany, situated in the Heidekreis district.
  • D. Oberholzer
    Oberholzer is a surname of Germanic origin, commonly found in German-speaking regions and among their diasporas.
  • E. Beyenburg
    Beyenburg is a historic district in the eastern part of Wuppertal, Germany, known for its medieval monastery, reservoir, and well-preserved village character.
  • 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_69ca82ecbdc481908a55cad8ca062d88 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7fd5027c81909724f25aa30bbe58 completed March 31, 2026, 8:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd95e4382081908634f31eb115e557 completed April 1, 2026, 10:02 p.m.
NEDg Description generation batch_69cda20edc00819084c4ea5c77e1eb4f completed April 1, 2026, 10:54 p.m.
NED2 Entity disambiguation (via description) batch_69cda62491b481908bb37fad80414d4d completed April 1, 2026, 11:11 p.m.
Created at: March 30, 2026, 5:57 p.m.