Triple

T7407311
Position Surface form Disambiguated ID Type / Status
Subject Ludwigshafen am Rhein E170906 entity
Predicate hasDistrict P459 FINISHED
Object Mundenheim
Mundenheim is a district of the industrial city of Ludwigshafen am Rhein in the German state of Rhineland-Palatinate.
 E662423 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: Mundenheim | Statement: [Ludwigshafen am Rhein, hasDistrict, Mundenheim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mundenheim
Context triple: [Ludwigshafen am Rhein, hasDistrict, Mundenheim]
  • A. Wittmund
    Wittmund is a small town in Lower Saxony, Germany, known as an administrative center in the East Frisia region.
  • B. Bergneustadt
    Bergneustadt is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Oberbergischer Kreis region and its traditional half-timbered architecture.
  • C. Schweinheim
    Schweinheim is a residential district within the Bad Godesberg borough of Bonn in western Germany.
  • D. Eulachstadt
    Eulachstadt is a nickname for the Swiss city of Winterthur, reflecting its historical association with the Eulach River and its development as an important industrial and cultural center.
  • E. Müggelheim
    Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
  • 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: Mundenheim
Triple: [Ludwigshafen am Rhein, hasDistrict, Mundenheim]
Generated description
Mundenheim is a district of the industrial city of Ludwigshafen am Rhein in the German state of Rhineland-Palatinate.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mundenheim
Target entity description: Mundenheim is a district of the industrial city of Ludwigshafen am Rhein in the German state of Rhineland-Palatinate.
  • A. Wittmund
    Wittmund is a small town in Lower Saxony, Germany, known as an administrative center in the East Frisia region.
  • B. Bergneustadt
    Bergneustadt is a small town in North Rhine-Westphalia, Germany, known for its location in the hilly Oberbergischer Kreis region and its traditional half-timbered architecture.
  • C. Schweinheim
    Schweinheim is a residential district within the Bad Godesberg borough of Bonn in western Germany.
  • D. Eulachstadt
    Eulachstadt is a nickname for the Swiss city of Winterthur, reflecting its historical association with the Eulach River and its development as an important industrial and cultural center.
  • E. Müggelheim
    Müggelheim is a village-like district in the southeastern part of Berlin, Germany, characterized by its forests, lakes, and tranquil, semi-rural atmosphere.
  • 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_69c68a6010108190925e5284de022660 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f298f2388190afc944c9bc78749a completed March 27, 2026, 9:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8111adbf48190a04df3cec1017b39 completed March 28, 2026, 5:34 p.m.
NEDg Description generation batch_69c81455967c81909757f42d976b535c completed March 28, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_69c814b413a08190b7e5e85d73ddb430 completed March 28, 2026, 5:49 p.m.
Created at: March 27, 2026, 3:10 p.m.