Triple

T13278349
Position Surface form Disambiguated ID Type / Status
Subject Dillingen district E316249 entity
Predicate containsMunicipality P852 FINISHED
Object Syrgenstein
Syrgenstein is a small municipality in the Bavarian region of southern Germany.
E1030863 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: Syrgenstein | Statement: [Dillingen district, containsMunicipality, Syrgenstein]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Syrgenstein
Context triple: [Dillingen district, containsMunicipality, Syrgenstein]
  • A. Syrgenstein
    Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
  • B. Gilserberg
    Gilserberg is a small municipality in the German state of Hesse, known for its rural character and location within the Schwalm-Eder district.
  • C. Burggen
    Burggen is a small rural municipality in the Bavarian region of Upper Bavaria in southern Germany.
  • D. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • E. Störnstein
    Störnstein is a small municipality in the Upper Palatinate region of Bavaria, Germany.
  • 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: Syrgenstein
Triple: [Dillingen district, containsMunicipality, Syrgenstein]
Generated description
Syrgenstein is a small municipality in the Bavarian region of southern Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Syrgenstein
Target entity description: Syrgenstein is a small municipality in the Bavarian region of southern Germany.
  • A. Syrgenstein
    Syrgenstein is a small municipality in the Heidenheim district of the German state of Baden-Württemberg.
  • B. Gilserberg
    Gilserberg is a small municipality in the German state of Hesse, known for its rural character and location within the Schwalm-Eder district.
  • C. Burggen
    Burggen is a small rural municipality in the Bavarian region of Upper Bavaria in southern Germany.
  • D. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • E. Störnstein
    Störnstein is a small municipality in the Upper Palatinate region of Bavaria, Germany.
  • 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_69d806b349908190a9a61dd9323bf153 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d99043fba88190872ede6f63e2fbcb completed April 11, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69f70a56fa048190b32dcef31b978d9c completed May 3, 2026, 8:41 a.m.
NEDg Description generation batch_69f70b8f325c819097fb5f221ba28b9a completed May 3, 2026, 8:47 a.m.
NED2 Entity disambiguation (via description) batch_69f70c4c6f908190b2ebc2a90b049e59 completed May 3, 2026, 8:50 a.m.
Created at: April 9, 2026, 9:26 p.m.