Triple

T6272742
Position Surface form Disambiguated ID Type / Status
Subject Grozny Airport E140576 entity
Predicate ICAOcode P419 FINISHED
Object URMG
URMG is the ICAO airport code assigned to Grozny Airport in the Chechen Republic of Russia.
 E580353 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: URMG | Statement: [Grozny Airport, ICAOcode, URMG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: URMG
Context triple: [Grozny Airport, ICAOcode, URMG]
  • A. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • B. UMAG
    UMAG is the commonly used abbreviation for the University of Magallanes, a public Chilean university located in the Magallanes and Chilean Antarctic Region.
  • C. UJ
    UJ is a major public university in Johannesburg, South Africa, known for its diverse academic programs and strong focus on research and innovation.
  • D. UMS
    UMS is the public university system that oversees multiple campuses and educational institutions across the state of Maine.
  • E. UMS
    UMS is a public university system in the U.S. state of Missouri that oversees multiple campuses, including the University of Missouri in Columbia.
  • 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: URMG
Triple: [Grozny Airport, ICAOcode, URMG]
Generated description
URMG is the ICAO airport code assigned to Grozny Airport in the Chechen Republic of Russia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: URMG
Target entity description: URMG is the ICAO airport code assigned to Grozny Airport in the Chechen Republic of Russia.
  • A. URM
    URM is the National Rail station code for Urmston railway station in Greater Manchester, England.
  • B. UMAG
    UMAG is the commonly used abbreviation for the University of Magallanes, a public Chilean university located in the Magallanes and Chilean Antarctic Region.
  • C. UJ
    UJ is a major public university in Johannesburg, South Africa, known for its diverse academic programs and strong focus on research and innovation.
  • D. UMS
    UMS is the public university system that oversees multiple campuses and educational institutions across the state of Maine.
  • E. UMS
    UMS is a public university system in the U.S. state of Missouri that oversees multiple campuses, including the University of Missouri in Columbia.
  • 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_69c008cabc4081909723e2547c9d6cc0 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c063bca5488190b9da3c037cfc7953 completed March 22, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69c2446ad060819094acd817ba5eadc9 completed March 24, 2026, 7:59 a.m.
NEDg Description generation batch_69c4fb6bb9bc8190a29ae09221aa5464 completed March 26, 2026, 9:24 a.m.
NED2 Entity disambiguation (via description) batch_69c4fc075dd881908230bb66d1445d5a completed March 26, 2026, 9:27 a.m.
Created at: March 22, 2026, 4:25 p.m.