Triple

T15233897
Position Surface form Disambiguated ID Type / Status
Subject Möhringen E364072 entity
Predicate adjacentTo P224 FINISHED
Object Plieningen
Plieningen is a district in the south of Stuttgart, Germany, known for its mix of rural character, historic village center, and proximity to Stuttgart Airport and the university campus in Vaihingen.
E1144302 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: Plieningen | Statement: [Möhringen, adjacentTo, Plieningen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Plieningen
Context triple: [Möhringen, adjacentTo, Plieningen]
  • A. Plön
    Plön is a small lakeside town in northern Germany known for its historic castle and scenic location in the Holstein Switzerland region.
  • B. Wedel
    Wedel is a town on the outskirts of Hamburg, Germany, known for being a suburban terminus of the city's S-Bahn network.
  • C. Seelingstädt
    Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
  • D. Plüderhausen
    Plüderhausen is a municipality in the German state of Baden-Württemberg, located in the Rems Valley east of Stuttgart.
  • E. Persingen
    Persingen is a small village in the Dutch province of Gelderland, often noted as one of the smallest villages in the Netherlands.
  • 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: Plieningen
Triple: [Möhringen, adjacentTo, Plieningen]
Generated description
Plieningen is a district in the south of Stuttgart, Germany, known for its mix of rural character, historic village center, and proximity to Stuttgart Airport and the university campus in Vaihingen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Plieningen
Target entity description: Plieningen is a district in the south of Stuttgart, Germany, known for its mix of rural character, historic village center, and proximity to Stuttgart Airport and the university campus in Vaihingen.
  • A. Plön
    Plön is a small lakeside town in northern Germany known for its historic castle and scenic location in the Holstein Switzerland region.
  • B. Wedel
    Wedel is a town on the outskirts of Hamburg, Germany, known for being a suburban terminus of the city's S-Bahn network.
  • C. Seelingstädt
    Seelingstädt is a village and subdivision of the town of Trebsen in the German state of Saxony.
  • D. Plüderhausen
    Plüderhausen is a municipality in the German state of Baden-Württemberg, located in the Rems Valley east of Stuttgart.
  • E. Persingen
    Persingen is a small village in the Dutch province of Gelderland, often noted as one of the smallest villages in the Netherlands.
  • 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_69d85a0ce24c81909c4d3b6475548c95 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e007d7237081908dc17900ee66b64f completed April 15, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69fedd3dd5a081909a1a7fceda648c29 completed May 9, 2026, 7:07 a.m.
NEDg Description generation batch_69fede8eed2c8190adb45306a1ec0faa completed May 9, 2026, 7:13 a.m.
NED2 Entity disambiguation (via description) batch_69fedef546708190bdeedd2c61fdbc86 completed May 9, 2026, 7:15 a.m.
Created at: April 10, 2026, 3:12 a.m.