Triple

T3775982
Position Surface form Disambiguated ID Type / Status
Subject Royal Malaysian Air Force E83309 entity
Predicate abbreviation P43 FINISHED
Object TUDM
TUDM is the Malay-language abbreviation for the Royal Malaysian Air Force, the aerial warfare branch of Malaysia’s armed forces.
 E386495 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: TUDM | Statement: [Royal Malaysian Air Force, abbreviation, TUDM]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TUDM
Context triple: [Royal Malaysian Air Force, abbreviation, TUDM]
  • A. TUDN
    TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
  • B. VUT
    VUT is the three-letter ISO 3166-1 alpha-3 country code assigned to Vanuatu.
  • C. ZTU
    ZTU is the IATA station code assigned to a specific passenger rail station in Miami, Florida, used for ticketing and travel logistics.
  • D. Brno University of Technology
    Brno University of Technology is a major Czech technical university known for its engineering, information technology, and architectural programs.
  • E. TUW
    TUW is the commonly used abbreviation for the Vienna University of Technology, a major technical and scientific research university in Vienna, Austria.
  • 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: TUDM
Triple: [Royal Malaysian Air Force, abbreviation, TUDM]
Generated description
TUDM is the Malay-language abbreviation for the Royal Malaysian Air Force, the aerial warfare branch of Malaysia’s armed forces.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TUDM
Target entity description: TUDM is the Malay-language abbreviation for the Royal Malaysian Air Force, the aerial warfare branch of Malaysia’s armed forces.
  • A. TUDN
    TUDN is a Spanish-language sports television network and media brand focused on soccer and other sports, primarily serving audiences in the United States and Mexico.
  • B. VUT
    VUT is the three-letter ISO 3166-1 alpha-3 country code assigned to Vanuatu.
  • C. ZTU
    ZTU is the IATA station code assigned to a specific passenger rail station in Miami, Florida, used for ticketing and travel logistics.
  • D. Brno University of Technology
    Brno University of Technology is a major Czech technical university known for its engineering, information technology, and architectural programs.
  • E. TUW
    TUW is the commonly used abbreviation for the Vienna University of Technology, a major technical and scientific research university in Vienna, Austria.
  • 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_69ad8b235e608190b5a2b1d1bfcef50b completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcc5be3c48190a72e840d8214bb74 completed March 8, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e53209888190823412fabacbc914 completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e60c23608190977198b6344ff09d completed March 14, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_69b4e686bf2c8190aac01d6c1014c1d4 completed March 14, 2026, 4:39 a.m.
Created at: March 8, 2026, 3:36 p.m.