Triple

T10066232
Position Surface form Disambiguated ID Type / Status
Subject Universiti Putra Malaysia E213108 entity
Predicate abbreviation P43 FINISHED
Object UPM
UPM is a leading Malaysian public research university known for its strong focus on agriculture, forestry, and related scientific disciplines.
 E838897 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: UPM | Statement: [Universiti Putra Malaysia, abbreviation, UPM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UPM
Context triple: [Universiti Putra Malaysia, abbreviation, UPM]
  • A. UPM
    UPM is the Polytechnic University of Madrid, a leading Spanish public university specializing in engineering, architecture, and technology.
  • B. Stora Enso
    Stora Enso is a Finnish-Swedish renewable materials and packaging company and one of the world’s largest producers of paper, pulp, and wood products.
  • C. International Paper
    International Paper is a leading global producer of renewable fiber-based packaging, pulp, and paper products.
  • D. Roseburg Forest Products
    Roseburg Forest Products is a privately owned wood products company based in Roseburg, Oregon, known for manufacturing lumber, engineered wood, and other building materials.
  • E. Georgia-Pacific
    Georgia-Pacific is a major American pulp and paper company known for producing tissue, packaging, building products, and related chemicals.
  • 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: UPM
Triple: [Universiti Putra Malaysia, abbreviation, UPM]
Generated description
UPM is a leading Malaysian public research university known for its strong focus on agriculture, forestry, and related scientific disciplines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UPM
Target entity description: UPM is a leading Malaysian public research university known for its strong focus on agriculture, forestry, and related scientific disciplines.
  • A. UPM
    UPM is the Polytechnic University of Madrid, a leading Spanish public university specializing in engineering, architecture, and technology.
  • B. Stora Enso
    Stora Enso is a Finnish-Swedish renewable materials and packaging company and one of the world’s largest producers of paper, pulp, and wood products.
  • C. International Paper
    International Paper is a leading global producer of renewable fiber-based packaging, pulp, and paper products.
  • D. Roseburg Forest Products
    Roseburg Forest Products is a privately owned wood products company based in Roseburg, Oregon, known for manufacturing lumber, engineered wood, and other building materials.
  • E. Georgia-Pacific
    Georgia-Pacific is a major American pulp and paper company known for producing tissue, packaging, building products, and related chemicals.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcff63a4c8190bb08a0428aafa189 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a84c3308190ba9286053c1017dc completed April 5, 2026, 5:23 p.m.
NEDg Description generation batch_69d29b985e308190a6ec3966e02f429c completed April 5, 2026, 5:27 p.m.
NED2 Entity disambiguation (via description) batch_69d29c5f64c881909aa3d093422fe475 completed April 5, 2026, 5:31 p.m.
Created at: March 30, 2026, 8:58 p.m.