Triple

T3838249
Position Surface form Disambiguated ID Type / Status
Subject Winterthur E93387 entity
Predicate demonym P191 FINISHED
Object Winterthurer
Winterthurer is the German term for an inhabitant or native of the Swiss city of Winterthur.
 E393776 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: Winterthurer | Statement: [Winterthur, demonym, Winterthurer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Winterthurer
Context triple: [Winterthur, demonym, Winterthurer]
  • A. Murten
    Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
  • B. Schwyz
    Schwyz is a historic canton in central Switzerland, known as one of the founding members that gave the Swiss Confederation its name.
  • C. Richterswil
    Richterswil is a picturesque municipality on the shores of Lake Zurich in the canton of Zurich, Switzerland.
  • D. Grenchen
    Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
  • E. Sigmaringen
    Sigmaringen is a historic town in southwestern Germany best known for its Hohenzollern castle and its role as a former seat of the Hohenzollern-Sigmaringen principality.
  • 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: Winterthurer
Triple: [Winterthur, demonym, Winterthurer]
Generated description
Winterthurer is the German term for an inhabitant or native of the Swiss city of Winterthur.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Winterthurer
Target entity description: Winterthurer is the German term for an inhabitant or native of the Swiss city of Winterthur.
  • A. Murten
    Murten is a historic bilingual town in the canton of Fribourg, Switzerland, known for its well-preserved medieval old town and lakeside setting on Lake Murten.
  • B. Schwyz
    Schwyz is a historic canton in central Switzerland, known as one of the founding members that gave the Swiss Confederation its name.
  • C. Richterswil
    Richterswil is a picturesque municipality on the shores of Lake Zurich in the canton of Zurich, Switzerland.
  • D. Grenchen
    Grenchen is a Swiss town in the canton of Solothurn known for its watchmaking industry and location at the foot of the Jura Mountains.
  • E. Sigmaringen
    Sigmaringen is a historic town in southwestern Germany best known for its Hohenzollern castle and its role as a former seat of the Hohenzollern-Sigmaringen principality.
  • 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_69aed96ce578819084ab16e3439976c9 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeeb9d11f081909fc51e84657ec7f1 completed March 9, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5040835dc81909ecf5053128f1cc7 completed March 14, 2026, 6:45 a.m.
NEDg Description generation batch_69b507cfee048190a41ad30f4ceaf6c8 completed March 14, 2026, 7:01 a.m.
NED2 Entity disambiguation (via description) batch_69b50857e9ec8190bb03f13c4573b779 completed March 14, 2026, 7:03 a.m.
Created at: March 9, 2026, 3:18 p.m.