Triple

T785335
Position Surface form Disambiguated ID Type / Status
Subject Wilhelm Bittrich E16588 entity
Predicate birthPlace P1 FINISHED
Object Wadern
Wadern is a small town in the Saarland region of western Germany, known for its rural character and location near the borders with Luxembourg and France.
 E96280 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: Wadern | Statement: [Wilhelm Bittrich, birthPlace, Wadern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wadern
Context triple: [Wilhelm Bittrich, birthPlace, Wadern]
  • A. Wiesen
    Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
  • B. Crombach
    Crombach is a village in the municipality of St. Vith in the German-speaking region of eastern Belgium.
  • C. Schöngarth
    Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
  • D. Fürth
    Fürth is a historic city in northern Bavaria, Germany, known for its well-preserved old town and proximity to Nuremberg within the Franconian metropolitan region.
  • E. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • 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: Wadern
Triple: [Wilhelm Bittrich, birthPlace, Wadern]
Generated description
Wadern is a small town in the Saarland region of western Germany, known for its rural character and location near the borders with Luxembourg and France.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wadern
Target entity description: Wadern is a small town in the Saarland region of western Germany, known for its rural character and location near the borders with Luxembourg and France.
  • A. Wiesen
    Wiesen is a small locality that forms one of the subdivisions of the town of Lichtenfels in Germany.
  • B. Crombach
    Crombach is a village in the municipality of St. Vith in the German-speaking region of eastern Belgium.
  • C. Schöngarth
    Schöngarth is a German surname most notably associated with Eberhard Schöngarth, a high-ranking Nazi SS officer and war criminal during World War II.
  • D. Fürth
    Fürth is a historic city in northern Bavaria, Germany, known for its well-preserved old town and proximity to Nuremberg within the Franconian metropolitan region.
  • E. Starnberg
    Starnberg is a lakeside town in Bavaria, Germany, known for its affluent residential character and scenic location on Lake Starnberg southwest of Munich.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a76b0d6c8190a09b1a0bd4a6eeec completed March 1, 2026, 8:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d7c2d488190a0c1802eb7c7491d completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a7883f3d98819082b01a9c57fe230c completed March 4, 2026, 1:17 a.m.
NED2 Entity disambiguation (via description) batch_69a78875f424819090d6e927864d39e0 completed March 4, 2026, 1:18 a.m.
Created at: March 1, 2026, 7:38 p.m.