Triple

T9719191
Position Surface form Disambiguated ID Type / Status
Subject Ministry for Foreign Affairs of Finland E235417 entity
Predicate abbreviation P43 FINISHED
Object UM
UM is the commonly used abbreviation for Finland’s Ministry for Foreign Affairs, the government body responsible for the country’s foreign policy and diplomatic relations.
E816965 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: UM | Statement: [Ministry for Foreign Affairs of Finland, abbreviation, UM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UM
Context triple: [Ministry for Foreign Affairs of Finland, abbreviation, UM]
  • A. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • B. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • C. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • D. UM
    UM is a public research university in Oxford, Mississippi, commonly known as "Ole Miss" and recognized for its academic programs and SEC athletics.
  • E. UM
    UM is the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
  • 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: UM
Triple: [Ministry for Foreign Affairs of Finland, abbreviation, UM]
Generated description
UM is the commonly used abbreviation for Finland’s Ministry for Foreign Affairs, the government body responsible for the country’s foreign policy and diplomatic relations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: UM
Target entity description: UM is the commonly used abbreviation for Finland’s Ministry for Foreign Affairs, the government body responsible for the country’s foreign policy and diplomatic relations.
  • A. UM
    UM is the commonly used abbreviation for Universitas Negeri Malang, a public university in Malang, Indonesia known for its strong focus on education and teacher training.
  • B. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • C. UM
    UM is the commonly used abbreviation for Maastricht University, a public research university located in Maastricht, the Netherlands.
  • D. UM
    UM is the ISO 3166-1 alpha-2 country code assigned to the United States Minor Outlying Islands, a group of small, mostly uninhabited U.S.-administered Pacific and Caribbean islands.
  • E. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • 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_69ca84d0123c819096f9dc3b6abb0881 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9e4022c4819097455f14dd9b1a77 completed April 1, 2026, 10:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19f9c4ff4819098ad941438abbe3c completed April 4, 2026, 11:32 p.m.
NEDg Description generation batch_69d1a0326dd081909e39e422c11cc08f completed April 4, 2026, 11:35 p.m.
NED2 Entity disambiguation (via description) batch_69d1a0c05aa88190a93b0b71bef5c417 completed April 4, 2026, 11:37 p.m.
Created at: March 30, 2026, 8:20 p.m.