Triple

T2765787
Position Surface form Disambiguated ID Type / Status
Subject Matariki Network of Universities E61333 entity
Predicate abbreviation P43 FINISHED
Object MNU
MNU is an international consortium of universities focused on collaboration in research, teaching, and community engagement, particularly around themes of sustainability and global citizenship.
 E298031 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: MNU | Statement: [Matariki Network of Universities, abbreviation, MNU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MNU
Context triple: [Matariki Network of Universities, abbreviation, MNU]
  • A. MNF
    MNF is the commonly used abbreviation for the Faculty of Science at the University of Zurich, encompassing its natural science departments and research institutes.
  • B. MNP
    MNP is the three-letter ISO 3166-1 alpha-3 country code assigned to the Northern Mariana Islands.
  • C. MNH
    MNH was a German company that played a key role in producing Panther tanks for the Wehrmacht during World War II.
  • D. MNR
    MNR is the abbreviated designation for the National Republican Navy, the maritime military force of the Italian Social Republic during World War II.
  • E. MU
    MU is the IATA airline designator assigned to China Eastern Airlines, one of China’s major carriers.
  • 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: MNU
Triple: [Matariki Network of Universities, abbreviation, MNU]
Generated description
MNU is an international consortium of universities focused on collaboration in research, teaching, and community engagement, particularly around themes of sustainability and global citizenship.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MNU
Target entity description: MNU is an international consortium of universities focused on collaboration in research, teaching, and community engagement, particularly around themes of sustainability and global citizenship.
  • A. MNF
    MNF is the commonly used abbreviation for the Faculty of Science at the University of Zurich, encompassing its natural science departments and research institutes.
  • B. MNP
    MNP is the three-letter ISO 3166-1 alpha-3 country code assigned to the Northern Mariana Islands.
  • C. MNH
    MNH was a German company that played a key role in producing Panther tanks for the Wehrmacht during World War II.
  • D. MNR
    MNR is the abbreviated designation for the National Republican Navy, the maritime military force of the Italian Social Republic during World War II.
  • E. MU
    MU is the IATA airline designator assigned to China Eastern Airlines, one of China’s major carriers.
  • 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_69ab4b7bab6c8190a5c2efef19a8ef34 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd55a96c8190a109a1f0e8752477 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc048bc8481908a6f70e034167c2a completed March 10, 2026, 6:55 a.m.
NEDg Description generation batch_69afc14239e48190ad20f660e88befcb completed March 10, 2026, 6:59 a.m.
NED2 Entity disambiguation (via description) batch_69afc202466c81908c300520173837dc completed March 10, 2026, 7:02 a.m.
Created at: March 6, 2026, 9:57 p.m.