Triple

T10434714
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Foreign Affairs of Lithuania E246008 entity
Predicate abbreviation P43 FINISHED
Object URM
URM is the commonly used Lithuanian abbreviation for the country’s Ministry of Foreign Affairs.
 E863970 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: URM | Statement: [Ministry of Foreign Affairs of Lithuania, abbreviation, URM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: URM
Context triple: [Ministry of Foreign Affairs of Lithuania, abbreviation, URM]
  • A. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • B. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • C. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • D. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • E. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • 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: URM
Triple: [Ministry of Foreign Affairs of Lithuania, abbreviation, URM]
Generated description
URM is the commonly used Lithuanian abbreviation for the country’s Ministry of Foreign Affairs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: URM
Target entity description: URM is the commonly used Lithuanian abbreviation for the country’s Ministry of Foreign Affairs.
  • A. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • B. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • C. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • D. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • 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_69d381bf3dc08190bf35a2643e4e8f22 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4ea67344c81909984ab99b0ef1e64 completed April 7, 2026, 11:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69d87ec1a0908190b5369ad55cf2bcb1 completed April 10, 2026, 4:38 a.m.
NEDg Description generation batch_69d886c325c4819089dac35eb26e7961 completed April 10, 2026, 5:12 a.m.
NED2 Entity disambiguation (via description) batch_69d88dc15ab481909011c5de93bbab14 completed April 10, 2026, 5:42 a.m.
Created at: April 6, 2026, 12:14 p.m.