Triple

T6870141
Position Surface form Disambiguated ID Type / Status
Subject Garching E158519 entity
Predicate hasPart P35 FINISHED
Object Hochbrück
Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
 E624968 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: Hochbrück | Statement: [Garching, hasPart, Hochbrück]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hochbrück
Context triple: [Garching, hasPart, Hochbrück]
  • A. Bersenbrück
    Bersenbrück is a small town in Lower Saxony, Germany, known for its historic abbey and its location on the river Hase.
  • B. Warthbrücken
    Warthbrücken is the German name for the Polish town of Koło, located in central Poland on the Warta River.
  • C. Seebruck
    Seebruck is a Bavarian lakeside village and popular holiday resort on the northern shore of Lake Chiemsee in southern Germany.
  • D. Quakenbrück
    Quakenbrück is a small historic town in Lower Saxony, Germany, known for its medieval architecture and location in the Artland region.
  • E. Holbeinsteg
    Holbeinsteg is a pedestrian suspension bridge over the River Main in Frankfurt, Germany, linking the museum district on the south bank with the city center.
  • 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: Hochbrück
Triple: [Garching, hasPart, Hochbrück]
Generated description
Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hochbrück
Target entity description: Hochbrück is a district of the Bavarian town of Garching near Munich, known for its industrial areas and proximity to major research and technology facilities.
  • A. Bersenbrück
    Bersenbrück is a small town in Lower Saxony, Germany, known for its historic abbey and its location on the river Hase.
  • B. Warthbrücken
    Warthbrücken is the German name for the Polish town of Koło, located in central Poland on the Warta River.
  • C. Seebruck
    Seebruck is a Bavarian lakeside village and popular holiday resort on the northern shore of Lake Chiemsee in southern Germany.
  • D. Quakenbrück
    Quakenbrück is a small historic town in Lower Saxony, Germany, known for its medieval architecture and location in the Artland region.
  • E. Holbeinsteg
    Holbeinsteg is a pedestrian suspension bridge over the River Main in Frankfurt, Germany, linking the museum district on the south bank with the city center.
  • 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_69c68831e3648190a643c328122e4d43 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d8aa47f48190bc7cad3cc652f530 completed March 27, 2026, 7:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c742a114008190be431f1e10d94501 completed March 28, 2026, 2:53 a.m.
NEDg Description generation batch_69c743328d148190814372ce5217f9cd completed March 28, 2026, 2:55 a.m.
NED2 Entity disambiguation (via description) batch_69c7443919ec819089040e50462864d1 completed March 28, 2026, 3 a.m.
Created at: March 27, 2026, 2:22 p.m.