Triple

T14662973
Position Surface form Disambiguated ID Type / Status
Subject Qatar Tourism Authority E344291 entity
Predicate alsoKnownAs P39 FINISHED
Object QTA
QTA is the acronym commonly used for the Qatar Tourism Authority, the government body responsible for promoting and regulating tourism in Qatar.
 E1112711 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: QTA | Statement: [Qatar Tourism Authority, alsoKnownAs, QTA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: QTA
Context triple: [Qatar Tourism Authority, alsoKnownAs, QTA]
  • A. QTA
    QTA is the official railway station code for Quetta Railway Station in Pakistan’s railway network.
  • B. TAQ
    TAQ is the station code for Tasqueña, a major terminal station on Mexico City’s Metro system.
  • C. ATQ
    ATQ is the IATA airport code for Sri Guru Ram Dass Jee International Airport serving Amritsar, India.
  • D. QVT
    QVT (Query/View/Transformation) is an OMG standard language for specifying model-to-model transformations in model-driven software engineering.
  • E. TQO
    TQO is the IATA airport code for Tulum International Airport, a commercial airport serving the Tulum region in Quintana Roo, Mexico.
  • 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: QTA
Triple: [Qatar Tourism Authority, alsoKnownAs, QTA]
Generated description
QTA is the acronym commonly used for the Qatar Tourism Authority, the government body responsible for promoting and regulating tourism in Qatar.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: QTA
Target entity description: QTA is the acronym commonly used for the Qatar Tourism Authority, the government body responsible for promoting and regulating tourism in Qatar.
  • A. QTA
    QTA is the official railway station code for Quetta Railway Station in Pakistan’s railway network.
  • B. TAQ
    TAQ is the station code for Tasqueña, a major terminal station on Mexico City’s Metro system.
  • C. ATQ
    ATQ is the IATA airport code for Sri Guru Ram Dass Jee International Airport serving Amritsar, India.
  • D. QVT
    QVT (Query/View/Transformation) is an OMG standard language for specifying model-to-model transformations in model-driven software engineering.
  • E. TQO
    TQO is the IATA airport code for Tulum International Airport, a commercial airport serving the Tulum region in Quintana Roo, Mexico.
  • 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_69d822e283fc8190a0e4c235cf880052 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb54ae5ac81908cc69891f280e5f7 completed April 14, 2026, 9:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdd5e4789481909a64622a1d284373 completed May 8, 2026, 12:24 p.m.
NEDg Description generation batch_69fdd9b5d7988190927e88feef6a972f completed May 8, 2026, 12:40 p.m.
NED2 Entity disambiguation (via description) batch_69fddaa56f4c8190ba56af6a7a56a201 completed May 8, 2026, 12:44 p.m.
Created at: April 10, 2026, 1:27 a.m.