Triple

T13754618
Position Surface form Disambiguated ID Type / Status
Subject Donau-Ries E330443 entity
Predicate containsMunicipality P852 FINISHED
Object Harburg (Schwaben)
Harburg (Schwaben) is a historic Bavarian town known for its well-preserved medieval castle and picturesque location on the Wörnitz River.
 E1060498 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: Harburg (Schwaben) | Statement: [Donau-Ries, containsMunicipality, Harburg (Schwaben)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Harburg (Schwaben)
Context triple: [Donau-Ries, containsMunicipality, Harburg (Schwaben)]
  • A. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • B. Herrenberg
    Herrenberg is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval center and proximity to the Schönbuch Nature Park.
  • C. Albershausen
    Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern Germany.
  • D. Höchheim
    Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
  • E. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • 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: Harburg (Schwaben)
Triple: [Donau-Ries, containsMunicipality, Harburg (Schwaben)]
Generated description
Harburg (Schwaben) is a historic Bavarian town known for its well-preserved medieval castle and picturesque location on the Wörnitz River.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Harburg (Schwaben)
Target entity description: Harburg (Schwaben) is a historic Bavarian town known for its well-preserved medieval castle and picturesque location on the Wörnitz River.
  • A. Hammelburg
    Hammelburg is a historic town in northern Bavaria, Germany, known as one of the country’s oldest wine-growing communities.
  • B. Herrenberg
    Herrenberg is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval center and proximity to the Schönbuch Nature Park.
  • C. Albershausen
    Albershausen is a small municipality in the German state of Baden-Württemberg, located in the Göppingen district in southern Germany.
  • D. Höchheim
    Höchheim is a small municipality in the Rhön-Grabfeld district of northern Bavaria, Germany.
  • E. Günsberg
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • 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_69d81c573f288190aa2403d484fa3d49 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de02179c948190a652cc8c586e418f completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7a859e6748190aa1899830a02b710 completed May 3, 2026, 7:56 p.m.
NEDg Description generation batch_69f7a91deb3c8190ad2be7f1ca99ac9b completed May 3, 2026, 7:59 p.m.
NED2 Entity disambiguation (via description) batch_69f7ad51c6808190afa80fc3622399bf completed May 3, 2026, 8:17 p.m.
Created at: April 9, 2026, 10:09 p.m.