Triple

T6155491
Position Surface form Disambiguated ID Type / Status
Subject Bad Godesberg E137309 entity
Predicate hasSubdistrict P747 FINISHED
Object Lannesdorf
Lannesdorf is a residential subdistrict of the Bad Godesberg borough in the city of Bonn, Germany.
 E573380 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: Lannesdorf | Statement: [Bad Godesberg, hasSubdistrict, Lannesdorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lannesdorf
Context triple: [Bad Godesberg, hasSubdistrict, Lannesdorf]
  • A. Chaudfontaine
    Chaudfontaine is a Belgian municipality in the Walloon region, noted for its mineral water springs and proximity to the city of Liège.
  • B. St. Vith
    St. Vith is a town in eastern Belgium that became a strategically important battleground during World War II, particularly noted for its role in the Battle of the Bulge.
  • C. Ottobeuren
    Ottobeuren is a market town in Bavaria, Germany, best known for its historic Benedictine abbey and impressive Baroque architecture.
  • D. Blegny
    Blegny is a municipality in eastern Belgium known for its historic coal mining heritage and rural character.
  • E. Bütgenbach
    Bütgenbach is a municipality in eastern Belgium’s German-speaking Community, known for its scenic lake, outdoor recreation, and proximity to the strategic Elsenborn Ridge.
  • 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: Lannesdorf
Triple: [Bad Godesberg, hasSubdistrict, Lannesdorf]
Generated description
Lannesdorf is a residential subdistrict of the Bad Godesberg borough in the city of Bonn, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lannesdorf
Target entity description: Lannesdorf is a residential subdistrict of the Bad Godesberg borough in the city of Bonn, Germany.
  • A. Chaudfontaine
    Chaudfontaine is a Belgian municipality in the Walloon region, noted for its mineral water springs and proximity to the city of Liège.
  • B. St. Vith
    St. Vith is a town in eastern Belgium that became a strategically important battleground during World War II, particularly noted for its role in the Battle of the Bulge.
  • C. Ottobeuren
    Ottobeuren is a market town in Bavaria, Germany, best known for its historic Benedictine abbey and impressive Baroque architecture.
  • D. Blegny
    Blegny is a municipality in eastern Belgium known for its historic coal mining heritage and rural character.
  • E. Bütgenbach
    Bütgenbach is a municipality in eastern Belgium’s German-speaking Community, known for its scenic lake, outdoor recreation, and proximity to the strategic Elsenborn Ridge.
  • 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_69c008a45d008190832a9e19f5d63406 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c05d01ddb0819085b5f5338b86a25d completed March 22, 2026, 9:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69c141867fac819093a3093aa8251eac completed March 23, 2026, 1:35 p.m.
NEDg Description generation batch_69c14794c3488190874b0a4d2c00514d completed March 23, 2026, 2 p.m.
NED2 Entity disambiguation (via description) batch_69c1480b10f08190891bfc8488bbaf16 completed March 23, 2026, 2:02 p.m.
Created at: March 22, 2026, 4:17 p.m.