Triple

T1569642
Position Surface form Disambiguated ID Type / Status
Subject Lokpriya Gopinath Bordoloi International Airport E33508 entity
Predicate ICAOcode P419 FINISHED
Object VEGT
VEGT is the ICAO airport code for Lokpriya Gopinath Bordoloi International Airport in Guwahati, India.
 E179155 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: VEGT | Statement: [Lokpriya Gopinath Bordoloi International Airport, ICAOcode, VEGT]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: VEGT
Context triple: [Lokpriya Gopinath Bordoloi International Airport, ICAOcode, VEGT]
  • A. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • B. Plante
    Plante is a French-origin surname commonly found in Canada and other Francophone regions, associated with several notable figures in sports, politics, and the arts.
  • C. VT
    VT is the standard two-letter postal abbreviation used to represent the U.S. state of Vermont.
  • D. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • E. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • 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: VEGT
Triple: [Lokpriya Gopinath Bordoloi International Airport, ICAOcode, VEGT]
Generated description
VEGT is the ICAO airport code for Lokpriya Gopinath Bordoloi International Airport in Guwahati, India.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: VEGT
Target entity description: VEGT is the ICAO airport code for Lokpriya Gopinath Bordoloi International Airport in Guwahati, India.
  • A. VE
    VE is the two-letter ISO 3166-1 alpha-2 country code assigned to Venezuela for international standardization and identification purposes.
  • B. Plante
    Plante is a French-origin surname commonly found in Canada and other Francophone regions, associated with several notable figures in sports, politics, and the arts.
  • C. VT
    VT is the standard two-letter postal abbreviation used to represent the U.S. state of Vermont.
  • D. VEN
    VEN is the three-letter ISO 3166-1 alpha-3 country code assigned to Venezuela for international identification and data standards.
  • E. VELO
    VELO is the high-precision vertex detector of the LHCb experiment at CERN, designed to measure particle trajectories very close to the proton–proton collision point.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908b67304819081ad555000e51197 completed March 5, 2026, 4:38 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad40263ef08190a968f6c822b5d483 completed March 8, 2026, 9:23 a.m.
NEDg Description generation batch_69ad40cb84d081908c6e1651989de716 completed March 8, 2026, 9:26 a.m.
NED2 Entity disambiguation (via description) batch_69ad41b1192c81909b89013d8296fd22 completed March 8, 2026, 9:30 a.m.
Created at: March 4, 2026, 7:27 p.m.