Triple

T22569289
Position Surface form Disambiguated ID Type / Status
Subject Mongolian Football Federation E558036 entity
Predicate fifaCountryCode P6278 FINISHED
Object MNG
MNG is the three-letter FIFA country code representing the Mongolia national football team in international competitions.
 E1542897 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: MNG | Statement: [Mongolian Football Federation, fifaCountryCode, MNG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MNG
Context triple: [Mongolian Football Federation, fifaCountryCode, MNG]
  • A. DNG
    DNG (Digital Negative) is an open, publicly documented raw image file format developed by Adobe for long-term archival and broad compatibility of digital photographs.
  • B. MPIMG
    MPIMG is a leading research institute of the Max Planck Society focused on advancing molecular genetics and genomics.
  • C. MoPNG
    MoPNG is the Indian government ministry responsible for overseeing the exploration, production, refining, distribution, and marketing of petroleum and natural gas in India.
  • D. PNG
    PNG is the three-letter ISO 3166-1 alpha-3 country code representing Papua New Guinea.
  • E. PNG
    PNG (Portable Network Graphics) is a widely used raster image format known for its lossless compression and support for transparency, commonly used for web graphics and digital images.
  • 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: MNG
Triple: [Mongolian Football Federation, fifaCountryCode, MNG]
Generated description
MNG is the three-letter FIFA country code representing the Mongolia national football team in international competitions.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MNG
Target entity description: MNG is the three-letter FIFA country code representing the Mongolia national football team in international competitions.
  • A. DNG
    DNG (Digital Negative) is an open, publicly documented raw image file format developed by Adobe for long-term archival and broad compatibility of digital photographs.
  • B. MPIMG
    MPIMG is a leading research institute of the Max Planck Society focused on advancing molecular genetics and genomics.
  • C. MoPNG
    MoPNG is the Indian government ministry responsible for overseeing the exploration, production, refining, distribution, and marketing of petroleum and natural gas in India.
  • D. PNG
    PNG is the three-letter ISO 3166-1 alpha-3 country code representing Papua New Guinea.
  • E. PNG
    PNG (Portable Network Graphics) is a widely used raster image format known for its lossless compression and support for transparency, commonly used for web graphics and digital images.
  • 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_69e11e5ae4ac8190b1f503457603d969 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15fac7db08190ba4660535571498d completed April 29, 2026, 1:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0b2d75f8fc8190af7282848379184d completed May 18, 2026, 3:17 p.m.
NEDg Description generation batch_6a0b364801cc81908204a937c1099728 completed May 18, 2026, 3:54 p.m.
NED2 Entity disambiguation (via description) batch_6a0b37998f888190bb53c2429760321a completed May 18, 2026, 4 p.m.
Created at: April 16, 2026, 8:52 p.m.