Triple

T5208141
Position Surface form Disambiguated ID Type / Status
Subject Englischer Garten E117562 entity
Predicate hasPart P35 FINISHED
Object Hirschau
Hirschau is a riverside meadow and recreational area within Munich’s Englischer Garten, known for its open green spaces, beer gardens, and walking paths along the Isar.
 E526961 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: Hirschau | Statement: [Englischer Garten, hasPart, Hirschau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hirschau
Context triple: [Englischer Garten, hasPart, Hirschau]
  • A. Hersbruck
    Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
  • B. Schaafheim
    Schaafheim is a municipality in the state of Hesse in central Germany.
  • C. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • D. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • E. Hettstadt
    Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
  • 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: Hirschau
Triple: [Englischer Garten, hasPart, Hirschau]
Generated description
Hirschau is a riverside meadow and recreational area within Munich’s Englischer Garten, known for its open green spaces, beer gardens, and walking paths along the Isar.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hirschau
Target entity description: Hirschau is a riverside meadow and recreational area within Munich’s Englischer Garten, known for its open green spaces, beer gardens, and walking paths along the Isar.
  • A. Hersbruck
    Hersbruck is a small historic town in the Franconian region of Bavaria, Germany, known for its picturesque setting in the Pegnitz Valley and traditional Bavarian architecture.
  • B. Schaafheim
    Schaafheim is a municipality in the state of Hesse in central Germany.
  • C. Hubersdorf
    Hubersdorf is a small municipality located in the canton of Solothurn in northwestern Switzerland.
  • D. Wilhelmsruh
    Wilhelmsruh is a locality in the borough of Pankow in Berlin, Germany, known for its residential character and historical ties to Berlin’s former border zone.
  • E. Hettstadt
    Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
  • 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_69bd4463dd3c81909966123f20b79d57 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7a6d70d081908c74e86b3bca9ba2 completed March 20, 2026, 4:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfce9ced708190b3ed8f9708c6f519 completed March 22, 2026, 11:12 a.m.
NEDg Description generation batch_69bfcf57ab108190a51e6fdd53b3f557 completed March 22, 2026, 11:15 a.m.
NED2 Entity disambiguation (via description) batch_69bfcfe331808190959ee32bae1b0bf1 completed March 22, 2026, 11:17 a.m.
Created at: March 20, 2026, 1:47 p.m.