Triple

T4033281
Position Surface form Disambiguated ID Type / Status
Subject Kuala Lumpur International Airport E83764 entity
Predicate hubFor P423 FINISHED
Object MASkargo
MASkargo is the air cargo division of Malaysia Airlines, providing freight and logistics services across a global network.
 E409252 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: MASkargo | Statement: [Kuala Lumpur International Airport, hubFor, MASkargo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MASkargo
Context triple: [Kuala Lumpur International Airport, hubFor, MASkargo]
  • A. Jambojet
    Jambojet is a Kenyan low-cost airline that operates domestic and regional flights as a subsidiary of Kenya Airways.
  • B. Marto
    Marto is a writer best known for contributing to the song "Young, Wild & Free."
  • C. Masass
    Masass was a leader associated with the Northwest Indian Confederacy, a coalition of Native American tribes that resisted U.S. expansion in the late 18th and early 19th centuries.
  • D. Argosy
    Argosy is a British pulp magazine best known for publishing adventure and genre fiction during the early to mid-20th century.
  • E. Orneta
    Orneta is a small historic town in northern Poland known for its medieval architecture and location within the picturesque Warmian-Masurian region.
  • 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: MASkargo
Triple: [Kuala Lumpur International Airport, hubFor, MASkargo]
Generated description
MASkargo is the air cargo division of Malaysia Airlines, providing freight and logistics services across a global network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MASkargo
Target entity description: MASkargo is the air cargo division of Malaysia Airlines, providing freight and logistics services across a global network.
  • A. Jambojet
    Jambojet is a Kenyan low-cost airline that operates domestic and regional flights as a subsidiary of Kenya Airways.
  • B. Marto
    Marto is a writer best known for contributing to the song "Young, Wild & Free."
  • C. Masass
    Masass was a leader associated with the Northwest Indian Confederacy, a coalition of Native American tribes that resisted U.S. expansion in the late 18th and early 19th centuries.
  • D. Argosy
    Argosy is a British pulp magazine best known for publishing adventure and genre fiction during the early to mid-20th century.
  • E. Orneta
    Orneta is a small historic town in northern Poland known for its medieval architecture and location within the picturesque Warmian-Masurian region.
  • 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_69aed92e29ac819080f7a98b594fec05 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb108fc0819080c8f41da2e558e0 completed March 9, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5563e11708190abc9ba55b1be43a5 completed March 14, 2026, 12:36 p.m.
NEDg Description generation batch_69b55a291d8c8190976e764011692ba0 completed March 14, 2026, 12:52 p.m.
NED2 Entity disambiguation (via description) batch_69b55a9ec7e88190bc5d165fd666f4b3 completed March 14, 2026, 12:54 p.m.
Created at: March 9, 2026, 3:36 p.m.