Triple

T12422280
Position Surface form Disambiguated ID Type / Status
Subject Ostafyevo International Airport E296804 entity
Predicate hasICAOcode P419 FINISHED
Object UUMO
UUMO is the ICAO airport code assigned to Ostafyevo International Airport near Moscow, Russia.
E981544 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: UUMO | Statement: [Ostafyevo International Airport, hasICAOcode, UUMO]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: UUMO
Context triple: [Ostafyevo International Airport, hasICAOcode, UUMO]
  • A. UMM
    UMM is a private Islamic university in Malang, Indonesia, affiliated with the Muhammadiyah organization and known for its wide range of academic programs.
  • B. UMM
    UMM is a small public liberal arts college campus of the University of Minnesota system located in Morris, Minnesota.
  • C. UMA
    UMA is a regional public university in Augusta, Maine, offering a range of undergraduate and professional programs with a focus on accessibility and nontraditional students.
  • D. UMA
    UMA is the acronym for the Arab Maghreb Union, a regional organization aimed at promoting economic and political cooperation among North African countries.
  • E. UMA
    UMA is the abbreviated name for the Unidad de Medios Aéreos, a specialized aerial media or air support unit typically involved in surveillance, transport, and operational support missions.
  • 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: UUMO
Triple: [Ostafyevo International Airport, hasICAOcode, UUMO]
Generated description
UUMO is the ICAO airport code assigned to Ostafyevo International Airport near Moscow, Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: UUMO
Target entity description: UUMO is the ICAO airport code assigned to Ostafyevo International Airport near Moscow, Russia.
  • A. UMM
    UMM is a private Islamic university in Malang, Indonesia, affiliated with the Muhammadiyah organization and known for its wide range of academic programs.
  • B. UMM
    UMM is a small public liberal arts college campus of the University of Minnesota system located in Morris, Minnesota.
  • C. UMA
    UMA is the acronym for the Arab Maghreb Union, a regional organization aimed at promoting economic and political cooperation among North African countries.
  • D. UMA
    UMA is a regional public university in Augusta, Maine, offering a range of undergraduate and professional programs with a focus on accessibility and nontraditional students.
  • E. UMA
    UMA is the abbreviated name for the Unidad de Medios Aéreos, a specialized aerial media or air support unit typically involved in surveillance, transport, and operational support missions.
  • 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_69d6ada0640c81908c061d7fb3d47786 completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d94d702b1481909db5f5bed6292ce0 completed April 10, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6349552fc81909fe73dea082e3a25 completed May 2, 2026, 5:29 p.m.
NEDg Description generation batch_69f6356c21908190b34d1324da8f8052 completed May 2, 2026, 5:33 p.m.
NED2 Entity disambiguation (via description) batch_69f63697d5b8819094728df472eb1914 completed May 2, 2026, 5:38 p.m.
Created at: April 8, 2026, 9:55 p.m.