Triple

T9223410
Position Surface form Disambiguated ID Type / Status
Subject Laichingen E221618 entity
Predicate hasSubdivision P747 FINISHED
Object Suppingen
Suppingen is a small village in the Swabian Alb region of Baden-Württemberg, Germany, that forms one of the districts of the town of Laichingen.
 E784970 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: Suppingen | Statement: [Laichingen, hasSubdivision, Suppingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Suppingen
Context triple: [Laichingen, hasSubdivision, Suppingen]
  • A. Pellinge
    Pellinge is a small island village in the Pellinge archipelago off the southern coast of Finland, known for its traditional fishing community and scenic Baltic Sea landscapes.
  • B. Sipplingen
    Sipplingen is a small lakeside municipality in southern Germany situated on the northern shore of Lake Constance in the state of Baden-Württemberg.
  • C. Kølpen
    Kølpen is one of the small islands in the Hirsholmene archipelago off the northeast coast of Jutland in Denmark.
  • D. Kistrand
    Kistrand is a small coastal settlement in northern Norway situated along the shores of the Porsangerfjorden.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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: Suppingen
Triple: [Laichingen, hasSubdivision, Suppingen]
Generated description
Suppingen is a small village in the Swabian Alb region of Baden-Württemberg, Germany, that forms one of the districts of the town of Laichingen.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Suppingen
Target entity description: Suppingen is a small village in the Swabian Alb region of Baden-Württemberg, Germany, that forms one of the districts of the town of Laichingen.
  • A. Pellinge
    Pellinge is a small island village in the Pellinge archipelago off the southern coast of Finland, known for its traditional fishing community and scenic Baltic Sea landscapes.
  • B. Sipplingen
    Sipplingen is a small lakeside municipality in southern Germany situated on the northern shore of Lake Constance in the state of Baden-Württemberg.
  • C. Kølpen
    Kølpen is one of the small islands in the Hirsholmene archipelago off the northeast coast of Jutland in Denmark.
  • D. Kistrand
    Kistrand is a small coastal settlement in northern Norway situated along the shores of the Porsangerfjorden.
  • E. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • 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_69ca83ec8db08190a9110df8232885d2 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccda7903208190b4e29a1591aab78a completed April 1, 2026, 8:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0664565748190a45cf382fca72cbe completed April 4, 2026, 1:15 a.m.
NEDg Description generation batch_69d06771ba808190a7b10f664425e76e completed April 4, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69d068087ab881908edcfd384a2e3f07 completed April 4, 2026, 1:23 a.m.
Created at: March 30, 2026, 7:28 p.m.