Triple

T8922266
Position Surface form Disambiguated ID Type / Status
Subject Unterdießen E212451 entity
Predicate hasAdministrativeCentre P1474 FINISHED
Object Unterdießen
Unterdießen is a small municipality in the district of Landsberg am Lech in Bavaria, Germany.
E765884 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: Unterdießen | Statement: [Unterdießen, hasAdministrativeCentre, Unterdießen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unterdießen
Context triple: [Unterdießen, hasAdministrativeCentre, Unterdießen]
  • A. Unterägeri
    Unterägeri is a Swiss municipality in the canton of Zug, known for its scenic location by Lake Ägeri and surrounding pre-Alpine landscapes.
  • B. Schmutter
    The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
  • C. Eningen unter Achalm
    Eningen unter Achalm is a municipality in the German state of Baden-Württemberg, situated at the foot of the Achalm mountain near the city of Reutlingen.
  • D. Wellblechpalast
    Wellblechpalast is an ice hockey arena in Berlin best known as the longtime home of the Eisbären Berlin before their move to larger, modern venues.
  • E. Am Hof
    Am Hof is a historic square in Vienna’s Innere Stadt district, known for its medieval origins, notable architecture, and role as a former center of civic and religious life.
  • 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: Unterdießen
Triple: [Unterdießen, hasAdministrativeCentre, Unterdießen]
Generated description
Unterdießen is a small municipality in the district of Landsberg am Lech in Bavaria, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Unterdießen
Target entity description: Unterdießen is a small municipality in the district of Landsberg am Lech in Bavaria, Germany.
  • A. Unterägeri
    Unterägeri is a Swiss municipality in the canton of Zug, known for its scenic location by Lake Ägeri and surrounding pre-Alpine landscapes.
  • B. Schmutter
    The Schmutter is a river in Bavaria, Germany, known as a regional tributary that flows through the Swabian landscape before joining the Wertach.
  • C. Eningen unter Achalm
    Eningen unter Achalm is a municipality in the German state of Baden-Württemberg, situated at the foot of the Achalm mountain near the city of Reutlingen.
  • D. Wellblechpalast
    Wellblechpalast is an ice hockey arena in Berlin best known as the longtime home of the Eisbären Berlin before their move to larger, modern venues.
  • E. Am Hof
    Am Hof is a historic square in Vienna’s Innere Stadt district, known for its medieval origins, notable architecture, and role as a former center of civic and religious life.
  • 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_69ca839481d48190b42b037e0d0f636c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc665143688190872c681f4299bd9f completed April 1, 2026, 12:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba4f094c81909a30d3be640ac9e0 completed April 3, 2026, 1:02 p.m.
NEDg Description generation batch_69cfbb5fe4f48190bd86c7606b1993bc completed April 3, 2026, 1:06 p.m.
NED2 Entity disambiguation (via description) batch_69cfbbcc6db8819080be0b2ae55837e2 completed April 3, 2026, 1:08 p.m.
Created at: March 30, 2026, 6:56 p.m.