Triple

T10215702
Position Surface form Disambiguated ID Type / Status
Subject Havelland E242434 entity
Predicate containsMunicipality P852 FINISHED
Object Nennhausen
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
E926830 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: Nennhausen | Statement: [Havelland, containsMunicipality, Nennhausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nennhausen
Context triple: [Havelland, containsMunicipality, Nennhausen]
  • A. Thannhausen
    Thannhausen is a small town in the Bavarian region of Swabia in southern Germany.
  • B. Niedernhausen
    Niedernhausen is a municipality in the Rheingau-Taunus district of Hesse, Germany, known for its wooded surroundings in the Taunus hills and convenient rail and road links to Wiesbaden and Frankfurt.
  • 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. Balzhausen
    Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
  • E. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • 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: Nennhausen
Triple: [Havelland, containsMunicipality, Nennhausen]
Generated description
Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nennhausen
Target entity description: Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
  • A. Thannhausen
    Thannhausen is a small town in the Bavarian region of Swabia in southern Germany.
  • B. Niedernhausen
    Niedernhausen is a municipality in the Rheingau-Taunus district of Hesse, Germany, known for its wooded surroundings in the Taunus hills and convenient rail and road links to Wiesbaden and Frankfurt.
  • 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. Balzhausen
    Balzhausen is a small municipality in the Bavarian region of Swabia in southern Germany.
  • E. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • 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_69d381ae26c48190985abd0e25ee5d04 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d3aa2894d0819095704449ecc2db6c completed April 6, 2026, 12:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69e5e877c6188190817fb30f2c9a07bf completed April 20, 2026, 8:48 a.m.
NEDg Description generation batch_69e5f1557e9c8190b53ce391793b2c7f completed April 20, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69e5f863bf7c81908969ed0a5b99f032 completed April 20, 2026, 9:56 a.m.
Created at: April 6, 2026, 11:05 a.m.