Triple

T6997372
Position Surface form Disambiguated ID Type / Status
Subject Universitas Negeri Malang E162249 entity
Predicate abbreviation P43 FINISHED
Object 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.
 E635272 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: [Universitas Negeri Malang, abbreviation, UM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UM
Context triple: [Universitas Negeri Malang, 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 the stock ticker symbol for MRU, the Canadian food and pharmacy retail company Metro Inc.
  • E. UM
    UM is a public research university in Oxford, Mississippi, commonly known as "Ole Miss" and recognized for its academic programs and SEC athletics.
  • 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: [Universitas Negeri Malang, abbreviation, UM]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UM
Target entity description: 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.
  • A. UM
    UM is the commonly used abbreviation for Maastricht University, a public research university located in Maastricht, the Netherlands.
  • 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 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

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_69c68857ffc08190857dc62cd5253777 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dbeef57881909245c8a5374a8111 completed March 27, 2026, 7:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a2465908190b69454f6215365b0 completed March 28, 2026, 5:41 a.m.
NEDg Description generation batch_69c76b67fc48819088ba80f1f84aa2f0 completed March 28, 2026, 5:47 a.m.
NED2 Entity disambiguation (via description) batch_69c76c46f9308190a7a1f0aa5284cef4 completed March 28, 2026, 5:51 a.m.
Created at: March 27, 2026, 2:33 p.m.