Triple

T16561048
Position Surface form Disambiguated ID Type / Status
Subject Calw district E402337 entity
Predicate containsTown P847 FINISHED
Object Neuweiler
Neuweiler is a small municipality in the Black Forest region of southwestern Germany, known for its rural character and forested landscapes.
E1225404 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: Neuweiler | Statement: [Calw district, containsTown, Neuweiler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Neuweiler
Context triple: [Calw district, containsTown, Neuweiler]
  • A. Baesweiler
    Baesweiler is a small town in western Germany’s North Rhine-Westphalia, situated near the city of Aachen and known for its historical ties to coal mining and regional industry.
  • B. Flaxweiler
    Flaxweiler is a small rural commune in eastern Luxembourg known for its agricultural landscape and proximity to the Moselle wine region.
  • C. Gnannenweiler
    Gnannenweiler is a small village in the municipality of Steinheim am Albuch in the Heidenheim district of Baden-Württemberg, Germany.
  • D. Lommersweiler
    Lommersweiler is a village and municipal section of the town of St. Vith in the German-speaking Community of eastern Belgium.
  • E. Bodersweier
    Bodersweier is a village and district of the town of Kehl in the Ortenaukreis region of Baden-Württemberg, Germany.
  • 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: Neuweiler
Triple: [Calw district, containsTown, Neuweiler]
Generated description
Neuweiler is a small municipality in the Black Forest region of southwestern Germany, known for its rural character and forested landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Neuweiler
Target entity description: Neuweiler is a small municipality in the Black Forest region of southwestern Germany, known for its rural character and forested landscapes.
  • A. Baesweiler
    Baesweiler is a small town in western Germany’s North Rhine-Westphalia, situated near the city of Aachen and known for its historical ties to coal mining and regional industry.
  • B. Flaxweiler
    Flaxweiler is a small rural commune in eastern Luxembourg known for its agricultural landscape and proximity to the Moselle wine region.
  • C. Gnannenweiler
    Gnannenweiler is a small village in the municipality of Steinheim am Albuch in the Heidenheim district of Baden-Württemberg, Germany.
  • D. Lommersweiler
    Lommersweiler is a village and municipal section of the town of St. Vith in the German-speaking Community of eastern Belgium.
  • E. Bodersweier
    Bodersweier is a village and district of the town of Kehl in the Ortenaukreis region of Baden-Württemberg, Germany.
  • 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_69d8838648088190acf97ef11fc3f61b completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3576d88288190b33543bea4706a36 completed April 18, 2026, 10:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0084ae0fb08190b809cd26b2e413aa completed May 10, 2026, 1:14 p.m.
NEDg Description generation batch_6a00853af11881908e0e61fb352e2e87 completed May 10, 2026, 1:16 p.m.
NED2 Entity disambiguation (via description) batch_6a0085f2fd2881908b31bd57790acb74 completed May 10, 2026, 1:19 p.m.
Created at: April 10, 2026, 5:15 a.m.