Triple

T15951620
Position Surface form Disambiguated ID Type / Status
Subject TAV Airports Holding E386830 entity
Predicate hasAbbreviation P43 FINISHED
Object TAV
TAV is a Turkish airport operations and services company known for managing and developing major airports in Turkey and internationally.
 E386830 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: TAV | Statement: [TAV Airports Holding, hasAbbreviation, TAV]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TAV
Context triple: [TAV Airports Holding, hasAbbreviation, TAV]
  • A. TAVHL
    TAVHL is the stock ticker symbol for TAV Airports Holding, a Turkish company that develops and operates airport terminals and related services.
  • B. Tavo
    Tavo is a common Spanish diminutive or nickname for the given name Gustavo.
  • C. BAV
    BAV is the ICAO airline designator assigned to the Vietnamese carrier Bamboo Airways.
  • D. BAV
    BAV is the German abbreviation for Switzerland’s Federal Office of Transport, the national authority responsible for regulating and overseeing the country’s public transport and rail systems.
  • E. BAV
    BAV is the abbreviated name of Germany’s former Federal Insurance Supervisory Office, the authority that oversaw and regulated the national insurance sector.
  • 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: TAV
Triple: [TAV Airports Holding, hasAbbreviation, TAV]
Generated description
TAV is a Turkish airport operations and services company known for managing and developing major airports in Turkey and internationally.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TAV
Target entity description: TAV is a Turkish airport operations and services company known for managing and developing major airports in Turkey and internationally.
  • A. TAVHL chosen
    TAVHL is the stock ticker symbol for TAV Airports Holding, a Turkish company that develops and operates airport terminals and related services.
  • B. Tavo
    Tavo is a common Spanish diminutive or nickname for the given name Gustavo.
  • C. BAV
    BAV is the ICAO airline designator assigned to the Vietnamese carrier Bamboo Airways.
  • D. BAV
    BAV is the German abbreviation for Switzerland’s Federal Office of Transport, the national authority responsible for regulating and overseeing the country’s public transport and rail systems.
  • E. BAV
    BAV is the abbreviated name of Germany’s former Federal Insurance Supervisory Office, the authority that oversaw and regulated the national insurance sector.
  • F. None of above.

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_69d86da882448190a82ea962fe343b79 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e156d59f5081909f6a81d578c4e2e8 completed April 16, 2026, 9:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe78a76481909622d7a2443cba4d completed May 9, 2026, 11:08 p.m.
NEDg Description generation batch_69ffbf7f96508190a0d9ea3622e1be06 completed May 9, 2026, 11:13 p.m.
NED2 Entity disambiguation (via description) batch_69ffc00c59f881909c42320f5dcc777b completed May 9, 2026, 11:15 p.m.
Created at: April 10, 2026, 4:53 a.m.