Triple

T6376115
Position Surface form Disambiguated ID Type / Status
Subject Medical University of Gdańsk E143468 entity
Predicate shortName P43 FINISHED
Object MUG
MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
 E588677 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: MUG | Statement: [Medical University of Gdańsk, shortName, MUG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MUG
Context triple: [Medical University of Gdańsk, shortName, MUG]
  • A. MUHA
    MUHA is the ICAO airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • B. Mook
    Mook is a surname most notably associated with Robby Mook, an American political strategist and campaign manager.
  • C. Mook
    Mook is a village in the Dutch province of Limburg, known for its scenic location along the Maas River near the German border.
  • D. Mizos
    Mizos are an indigenous Tibeto-Burman–speaking ethnic group primarily inhabiting the hilly regions of Northeast India’s Mizoram state and neighboring areas of Myanmar and Bangladesh, known for their distinct culture, festivals, and Christian-majority society.
  • E. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • 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: MUG
Triple: [Medical University of Gdańsk, shortName, MUG]
Generated description
MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MUG
Target entity description: MUG is the commonly used abbreviation for the Medical University of Gdańsk, a major medical education and research institution in Poland.
  • A. MUHA
    MUHA is the ICAO airport code for José Martí International Airport, the main international gateway serving Havana, Cuba.
  • B. Mook
    Mook is a surname most notably associated with Robby Mook, an American political strategist and campaign manager.
  • C. Mook
    Mook is a village in the Dutch province of Limburg, known for its scenic location along the Maas River near the German border.
  • D. Mizos
    Mizos are an indigenous Tibeto-Burman–speaking ethnic group primarily inhabiting the hilly regions of Northeast India’s Mizoram state and neighboring areas of Myanmar and Bangladesh, known for their distinct culture, festivals, and Christian-majority society.
  • E. Mugatu
    Mugatu is the flamboyant, villainous fashion designer portrayed by Will Ferrell in the comedy film "Zoolander."
  • 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_69c008d9f4348190ab598a2913259a1c completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0683bfc7081908b15c3c9a3c72e7b completed March 22, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c62d9dd9dc8190b2aca25feda3e690 completed March 27, 2026, 7:11 a.m.
NEDg Description generation batch_69c62fb982088190ab4ccbd5ff23740d completed March 27, 2026, 7:20 a.m.
NED2 Entity disambiguation (via description) batch_69c6302e2f008190bd7ccdfbcddb3c07 completed March 27, 2026, 7:22 a.m.
Created at: March 22, 2026, 4:33 p.m.