Triple

T17023468
Position Surface form Disambiguated ID Type / Status
Subject Siegen-Wittgenstein E413003 entity
Predicate contains P35 FINISHED
Object Wilnsdorf
Wilnsdorf is a municipality in the district of Siegen-Wittgenstein in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Siegen.
E1248935 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: Wilnsdorf | Statement: [Siegen-Wittgenstein, contains, Wilnsdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wilnsdorf
Context triple: [Siegen-Wittgenstein, contains, Wilnsdorf]
  • A. Wietzendorf
    Wietzendorf is a small municipality in Lower Saxony, Germany, known for its rural character and location in the Lüneburg Heath region.
  • B. Perasdorf
    Perasdorf is a small municipality in the Straubing-Bogen district of Lower Bavaria, Germany.
  • C. Teesdorf
    Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
  • D. Ruppersdorf
    Ruppersdorf is a small locality in Germany, historically part of East Prussia, known as the birthplace of German general Otto Lasch.
  • E. Teisendorf
    Teisendorf is a market town in southeastern Bavaria, Germany, known for its rural Alpine setting and traditional Bavarian 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: Wilnsdorf
Triple: [Siegen-Wittgenstein, contains, Wilnsdorf]
Generated description
Wilnsdorf is a municipality in the district of Siegen-Wittgenstein in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Siegen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Wilnsdorf
Target entity description: Wilnsdorf is a municipality in the district of Siegen-Wittgenstein in North Rhine-Westphalia, Germany, known for its rural character and proximity to the city of Siegen.
  • A. Wietzendorf
    Wietzendorf is a small municipality in Lower Saxony, Germany, known for its rural character and location in the Lüneburg Heath region.
  • B. Perasdorf
    Perasdorf is a small municipality in the Straubing-Bogen district of Lower Bavaria, Germany.
  • C. Teesdorf
    Teesdorf is a municipality in Lower Austria known for its motorsport testing facilities and rural setting south of Vienna.
  • D. Ruppersdorf
    Ruppersdorf is a small locality in Germany, historically part of East Prussia, known as the birthplace of German general Otto Lasch.
  • E. Teisendorf
    Teisendorf is a market town in southeastern Bavaria, Germany, known for its rural Alpine setting and traditional Bavarian 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d2abbc81908943becf5f539fc6 completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a012ed0b78481909a11c1529db6c1cd completed May 11, 2026, 1:20 a.m.
NEDg Description generation batch_6a012f9339d88190a8976240f5b2a8d8 completed May 11, 2026, 1:23 a.m.
NED2 Entity disambiguation (via description) batch_6a01301a4fbc8190bbd5b5b9bad814d3 completed May 11, 2026, 1:25 a.m.
Created at: April 10, 2026, 5:33 a.m.