Triple

T657497
Position Surface form Disambiguated ID Type / Status
Subject Lichtenfels E11679 entity
Predicate hasSubdivision P747 FINISHED
Object Gartenstadt
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
E87116 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: Gartenstadt | Statement: [Lichtenfels, hasSubdivision, Gartenstadt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gartenstadt
Context triple: [Lichtenfels, hasSubdivision, Gartenstadt]
  • A. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • B. Alt-Mariendorf
    Alt-Mariendorf is a Berlin U-Bahn station in the Mariendorf district that serves as the southern terminus of line U6.
  • C. Dessau-Törten housing estate
    The Dessau-Törten housing estate is a pioneering modernist residential development in Dessau, Germany, designed in the 1920s as a large-scale social housing project associated with the Bauhaus movement.
  • D. Ronsdorf
    Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
  • E. Stadtmitte
    Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
  • 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: Gartenstadt
Triple: [Lichtenfels, hasSubdivision, Gartenstadt]
Generated description
Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Gartenstadt
Target entity description: Gartenstadt is a residential district of the Upper Franconian town of Lichtenfels in Bavaria, Germany.
  • A. Schöneberg
    Schöneberg is a district of Berlin, Germany, historically notable as the site of John F. Kennedy’s famous “Ich bin ein Berliner” speech.
  • B. Alt-Mariendorf
    Alt-Mariendorf is a Berlin U-Bahn station in the Mariendorf district that serves as the southern terminus of line U6.
  • C. Dessau-Törten housing estate
    The Dessau-Törten housing estate is a pioneering modernist residential development in Dessau, Germany, designed in the 1920s as a large-scale social housing project associated with the Bauhaus movement.
  • D. Ronsdorf
    Ronsdorf is a district of the German city of Wuppertal in North Rhine-Westphalia, historically known as an independent town in the Bergisches Land region.
  • E. Stadtmitte
    Stadtmitte is a central Berlin U-Bahn station serving as an important interchange and access point to the city’s historic Mitte district.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fa55e048190bd9913c6c31772d0 completed March 1, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69a63747e47481909877b49507b67c2c completed March 3, 2026, 1:20 a.m.
NEDg Description generation batch_69a645cff2a481908aa0b0cfde78c929 completed March 3, 2026, 2:22 a.m.
NED2 Entity disambiguation (via description) batch_69a64656026c8190834af887720f3a0a completed March 3, 2026, 2:24 a.m.
Created at: March 1, 2026, 7:36 p.m.