Triple

T9123404
Position Surface form Disambiguated ID Type / Status
Subject LIP E218913 entity
Predicate usedInTown P87209 FINISHED
Object Schieder-Schwalenberg
Schieder-Schwalenberg is a small town in the Lippe district of North Rhine-Westphalia, Germany, known for its historic architecture and scenic location in the Teutoburg Forest region.
E779632 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: Schieder-Schwalenberg | Statement: [LIP, usedInTown, Schieder-Schwalenberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Schieder-Schwalenberg
Context triple: [LIP, usedInTown, Schieder-Schwalenberg]
  • A. Niederfeld
    Niederfeld is a district within the Mannheim borough of Neckarau in the German state of Baden-Württemberg.
  • B. Rheinhausen
    Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
  • C. Gailingen
    Gailingen is a village in the German municipality of Gailingen am Hochrhein in the state of Baden-Württemberg, near the Swiss border along the High Rhine.
  • D. Pfullendorf
    Pfullendorf is a historic town in the state of Baden-Württemberg in southern Germany, known for its well-preserved medieval old town.
  • E. Schattdorf
    Schattdorf is a Swiss municipality located in the central Alpine canton of Uri.
  • 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: Schieder-Schwalenberg
Triple: [LIP, usedInTown, Schieder-Schwalenberg]
Generated description
Schieder-Schwalenberg is a small town in the Lippe district of North Rhine-Westphalia, Germany, known for its historic architecture and scenic location in the Teutoburg Forest region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Schieder-Schwalenberg
Target entity description: Schieder-Schwalenberg is a small town in the Lippe district of North Rhine-Westphalia, Germany, known for its historic architecture and scenic location in the Teutoburg Forest region.
  • A. Niederfeld
    Niederfeld is a district within the Mannheim borough of Neckarau in the German state of Baden-Württemberg.
  • B. Rheinhausen
    Rheinhausen is a district of the German city of Duisburg, located on the western bank of the Rhine in North Rhine-Westphalia.
  • C. Gailingen
    Gailingen is a village in the German municipality of Gailingen am Hochrhein in the state of Baden-Württemberg, near the Swiss border along the High Rhine.
  • D. Pfullendorf
    Pfullendorf is a historic town in the state of Baden-Württemberg in southern Germany, known for its well-preserved medieval old town.
  • E. Schattdorf
    Schattdorf is a Swiss municipality located in the central Alpine canton of Uri.
  • 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_69ca83dddd548190983b96c664f7f367 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8b5fa188190be6465e74cf26915 completed April 1, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0308ff628819083f02bf71eb40c5b completed April 3, 2026, 9:26 p.m.
NEDg Description generation batch_69d0318ef52c8190bfa0bef6a8d41daa completed April 3, 2026, 9:30 p.m.
NED2 Entity disambiguation (via description) batch_69d03571c4648190bd546152c61c55a5 completed April 3, 2026, 9:47 p.m.
Created at: March 30, 2026, 7:17 p.m.