Triple

T14724543
Position Surface form Disambiguated ID Type / Status
Subject Peine E345903 entity
Predicate hasSubdivision P747 FINISHED
Object Essinghausen
Essinghausen is a village and locality that forms part of the town of Peine in Lower Saxony, Germany.
E1172826 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: Essinghausen | Statement: [Peine, hasSubdivision, Essinghausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Essinghausen
Context triple: [Peine, hasSubdivision, Essinghausen]
  • A. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • B. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • C. Nennhausen
    Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
  • D. 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.
  • E. 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.
  • 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: Essinghausen
Triple: [Peine, hasSubdivision, Essinghausen]
Generated description
Essinghausen is a village and locality that forms part of the town of Peine in Lower Saxony, Germany.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Essinghausen
Target entity description: Essinghausen is a village and locality that forms part of the town of Peine in Lower Saxony, Germany.
  • A. Ochsenhausen
    Ochsenhausen is a small historic town in the German state of Baden-Württemberg, best known for its former Benedictine monastery, Ochsenhausen Abbey.
  • B. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • C. Nennhausen
    Nennhausen is a rural municipality in the Havelland district of Brandenburg, Germany, known for its historic manor house and surrounding natural landscapes.
  • D. 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.
  • E. 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.
  • 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_69d822e5911c8190ba589f957dbd9ba7 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec25e9a14819081fa06fc601f295d completed April 14, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff755bbe608190a9a565218eee7005 completed May 9, 2026, 5:56 p.m.
NEDg Description generation batch_69ff762c0c548190a80392e1e83f68cc completed May 9, 2026, 6 p.m.
NED2 Entity disambiguation (via description) batch_69ff76fbcd188190b431bf277304aeea completed May 9, 2026, 6:03 p.m.
Created at: April 10, 2026, 1:29 a.m.