Triple

T9439517
Position Surface form Disambiguated ID Type / Status
Subject Lahn-Dill-Kreis E227605 entity
Predicate hasMunicipality P847 FINISHED
Object Ehringshausen
Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
E830302 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: Ehringshausen | Statement: [Lahn-Dill-Kreis, hasMunicipality, Ehringshausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ehringshausen
Context triple: [Lahn-Dill-Kreis, hasMunicipality, Ehringshausen]
  • A. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • B. Barsinghausen
    Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
  • C. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • D. Oerlinghausen
    Oerlinghausen is a small town in the German state of North Rhine-Westphalia, known for its scenic Teutoburg Forest surroundings and historical roots.
  • E. Borgholzhausen
    Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
  • 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: Ehringshausen
Triple: [Lahn-Dill-Kreis, hasMunicipality, Ehringshausen]
Generated description
Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ehringshausen
Target entity description: Ehringshausen is a municipality in the Lahn-Dill district of the German state of Hesse.
  • A. Gevelsberg
    Gevelsberg is a town in North Rhine-Westphalia, Germany, situated in the Ennepe-Ruhr district within the Ruhr metropolitan region.
  • B. Barsinghausen
    Barsinghausen is a town in Lower Saxony, Germany, located near Hanover and known historically for its mining industry and proximity to the Deister hills.
  • C. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • D. Oerlinghausen
    Oerlinghausen is a small town in the German state of North Rhine-Westphalia, known for its scenic Teutoburg Forest surroundings and historical roots.
  • E. Borgholzhausen
    Borgholzhausen is a small town in North Rhine-Westphalia, Germany, known for its location on the Teutoburg Forest and its historical ties to the former County of Ravensberg.
  • 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_69ca843884488190ad6cbe0153088234 completed March 30, 2026, 2:10 p.m.
NER Named-entity recognition batch_69cd7ee1c8c48190a2ae8673eee07e9a completed April 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69d22840c4548190b1610e2c3cec6220 completed April 5, 2026, 9:15 a.m.
NEDg Description generation batch_69d229f496c48190bf3bca109b3bc62b completed April 5, 2026, 9:23 a.m.
NED2 Entity disambiguation (via description) batch_69d22a8494f481909bd6b4936b32679e completed April 5, 2026, 9:25 a.m.
Created at: March 30, 2026, 7:50 p.m.