Triple

T10054073
Position Surface form Disambiguated ID Type / Status
Subject Combined Nomenclature of the European Union E208818 entity
Predicate abbreviation P43 FINISHED
Object CN
CN is the European Union’s standardized system for classifying goods in customs and trade statistics, used to determine tariffs and collect trade data.
 E837024 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: CN | Statement: [Combined Nomenclature of the European Union, abbreviation, CN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CN
Context triple: [Combined Nomenclature of the European Union, abbreviation, CN]
  • A. CN
    CN is a major Canadian freight railway company that operates an extensive rail network across Canada and into the United States.
  • B. CN
    CN is the commonly used abbreviation for Monaco’s National Council, the unicameral legislative body of the Principality.
  • C. CN
    CN is the vehicle registration code used for County Cavan in Ireland.
  • D. CN
    CN is the vehicle registration code used on license plates for the Province of Cuneo in Italy.
  • E. Simplified Chinese
    Simplified Chinese is a standardized form of written Chinese that uses characters with reduced strokes, primarily employed in mainland China and Singapore.
  • 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: CN
Triple: [Combined Nomenclature of the European Union, abbreviation, CN]
Generated description
CN is the European Union’s standardized system for classifying goods in customs and trade statistics, used to determine tariffs and collect trade data.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CN
Target entity description: CN is the European Union’s standardized system for classifying goods in customs and trade statistics, used to determine tariffs and collect trade data.
  • A. CN
    CN is a major Canadian freight railway company that operates an extensive rail network across Canada and into the United States.
  • B. CN
    CN is the commonly used abbreviation for Monaco’s National Council, the unicameral legislative body of the Principality.
  • C. CN
    CN is the vehicle registration code used for County Cavan in Ireland.
  • D. CN
    CN is the vehicle registration code used on license plates for the Province of Cuneo in Italy.
  • E. Simplified Chinese
    Simplified Chinese is a standardized form of written Chinese that uses characters with reduced strokes, primarily employed in mainland China and Singapore.
  • 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfab39408190ac728fe156eed658 completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d282a3d3148190908f02a9588b700e completed April 5, 2026, 3:41 p.m.
NEDg Description generation batch_69d2839311508190a59c1d393cc38785 completed April 5, 2026, 3:45 p.m.
NED2 Entity disambiguation (via description) batch_69d2843fff188190a21ced57523c329a completed April 5, 2026, 3:48 p.m.
Created at: March 30, 2026, 8:57 p.m.