Triple

T16815055
Position Surface form Disambiguated ID Type / Status
Subject Miyagi University of Education E408721 entity
Predicate abbreviation P43 FINISHED
Object MUE
MUE is a Japanese national university in Miyagi Prefecture specializing in teacher education and educational research.
 E1234946 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: MUE | Statement: [Miyagi University of Education, abbreviation, MUE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MUE
Context triple: [Miyagi University of Education, abbreviation, MUE]
  • A. MUH
    MUH is the IATA airport code for Marsa Matruh International Airport, which serves the coastal city of Mersa Matruh in Egypt.
  • B. MUHA
    MUHA is the ICAO airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • C. MUF
    MUF is the youth wing of Sweden's Moderate Party, engaging young people in center-right politics and policy issues.
  • D. MUG
    MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
  • E. MU
    MU is the common abbreviation for Masaryk University, a major public research university located in Brno, Czech Republic.
  • 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: MUE
Triple: [Miyagi University of Education, abbreviation, MUE]
Generated description
MUE is a Japanese national university in Miyagi Prefecture specializing in teacher education and educational research.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MUE
Target entity description: MUE is a Japanese national university in Miyagi Prefecture specializing in teacher education and educational research.
  • A. MUH
    MUH is the IATA airport code for Marsa Matruh International Airport, which serves the coastal city of Mersa Matruh in Egypt.
  • B. MUHA
    MUHA is the ICAO airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • C. MUF
    MUF is the youth wing of Sweden's Moderate Party, engaging young people in center-right politics and policy issues.
  • D. MUG
    MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
  • E. MU
    MU is the common abbreviation for Masaryk University, a major public research university located in Brno, Czech Republic.
  • 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_69d88394566c8190b3dcbdc72935f7fa completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b2e0e05081908bd5eaa64abe133d completed April 18, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b2946ddc81908b1e7c662dc943ff completed May 10, 2026, 4:30 p.m.
NEDg Description generation batch_6a00b3aafac08190b3e0181780f45392 completed May 10, 2026, 4:34 p.m.
NED2 Entity disambiguation (via description) batch_6a00b466ecd08190b7b5ee54476631ab completed May 10, 2026, 4:37 p.m.
Created at: April 10, 2026, 5:23 a.m.