Triple

T23137242
Position Surface form Disambiguated ID Type / Status
Subject Ministry of Tourism (Brazil) E577355 entity
Predicate shortName P43 FINISHED
Object MTur
MTur is the commonly used abbreviation for Brazil’s Ministry of Tourism, the federal government body responsible for tourism policy and promotion in the country.
 E1574891 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: MTur | Statement: [Ministry of Tourism (Brazil), shortName, MTur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MTur
Context triple: [Ministry of Tourism (Brazil), shortName, MTur]
  • A. MTS
    MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
  • B. MTS
    MTS is the three-letter National Rail station code for Montrose railway station in Angus, Scotland.
  • C. MTS
    MTS was a Canadian telecommunications company that provided phone, internet, and related services, primarily in Manitoba, before being acquired and rebranded as Bell MTS.
  • D. MTJ
    MTJ is the commonly used abbreviation for the Metro Jets, an American junior ice hockey team.
  • E. MTJ
    MTJ is the IATA airport code for Montrose Regional Airport, a commercial airport serving the Montrose and Telluride areas in western Colorado, USA.
  • 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: MTur
Triple: [Ministry of Tourism (Brazil), shortName, MTur]
Generated description
MTur is the commonly used abbreviation for Brazil’s Ministry of Tourism, the federal government body responsible for tourism policy and promotion in the country.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MTur
Target entity description: MTur is the commonly used abbreviation for Brazil’s Ministry of Tourism, the federal government body responsible for tourism policy and promotion in the country.
  • A. MTS
    MTS is the three-letter National Rail station code for Montrose railway station in Angus, Scotland.
  • B. MTS
    MTS is a public transportation agency that operates bus and rail services in the San Diego metropolitan area of California.
  • C. MTS
    MTS was a Canadian telecommunications company that provided phone, internet, and related services, primarily in Manitoba, before being acquired and rebranded as Bell MTS.
  • D. MTJ
    MTJ is the commonly used abbreviation for the Metro Jets, an American junior ice hockey team.
  • E. MTJ
    MTJ is the railway station code for Mathura Junction, a major rail hub in the Indian state of Uttar Pradesh.
  • 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_69e245f8e6248190ba3d58e068b4dccb completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18e8c33308190a44f98a7aab3b670 completed April 29, 2026, 4:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c308bbfb481909bb609337bc510e3 completed May 19, 2026, 9:42 a.m.
NEDg Description generation batch_6a0c34d070ec81908d5f6b7583696c02 completed May 19, 2026, 10 a.m.
NED2 Entity disambiguation (via description) batch_6a0c3551922481908520bf706ad8d7eb completed May 19, 2026, 10:02 a.m.
Created at: April 17, 2026, 4 p.m.