Triple

T681364
Position Surface form Disambiguated ID Type / Status
Subject Malaysia Airlines E13188 entity
Predicate ICAOCode P419 FINISHED
Object MAS
MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
 E82362 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: MAS | Statement: [Malaysia Airlines, ICAOCode, MAS]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MAS
Context triple: [Malaysia Airlines, ICAOCode, MAS]
  • A. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • B. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • C. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • D. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • E. LAS
    LAS is the commonly used abbreviation for the Arab League, a regional organization of Arab countries in and around North Africa, the Horn of Africa, and the Middle East.
  • 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: MAS
Triple: [Malaysia Airlines, ICAOCode, MAS]
Generated description
MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MAS
Target entity description: MAS is the ICAO airline designator used to identify Malaysia Airlines in international aviation operations.
  • A. MSA
    MSA is the standardized, literary form of Arabic used in formal writing, media, education, and official communication across the Arab world.
  • B. MSA
    MSA is the common abbreviation for the Master Settlement Agreement, a landmark 1998 legal settlement between major U.S. tobacco companies and state attorneys general that reshaped tobacco advertising and funded public health initiatives.
  • C. MAR
    MAR is the three-letter ISO 3166-1 alpha-3 country code assigned to Morocco.
  • D. MS
    MS is the official two-letter United States Postal Service abbreviation for the state of Mississippi.
  • E. LAS
    LAS is the commonly used abbreviation for the Arab League, a regional organization of Arab countries in and around North Africa, the Horn of Africa, and the Middle East.
  • 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_69a4933d3bf88190972041cd8cf143b9 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a4a06e294c8190873116a3253e04f9 completed March 1, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c3a5701c8190810e5e52bc2b61f7 completed March 2, 2026, 5:06 p.m.
NEDg Description generation batch_69a5cd8acc888190b9bb80198bce5d00 completed March 2, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_69a5ce6232e08190a8dba769f173f431 completed March 2, 2026, 5:52 p.m.
Created at: March 1, 2026, 7:36 p.m.