Triple

T16473074
Position Surface form Disambiguated ID Type / Status
Subject Vaudherland E400114 entity
Predicate mayor P185 FINISHED
Object Dominique Baert
Dominique Baert is a French politician known for serving as a long-time local mayor and member of the National Assembly.
 E1216563 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: Dominique Baert | Statement: [Vaudherland, mayor, Dominique Baert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dominique Baert
Context triple: [Vaudherland, mayor, Dominique Baert]
  • A. Kim Mestdagh
    Kim Mestdagh is a Belgian professional basketball player known as a key shooting guard for Belgium’s national team and for her successful international club career.
  • B. Els Vandevorst
    Els Vandevorst is a Dutch film producer known for her work on acclaimed European and international feature films.
  • C. Cécile Jodogne
    Cécile Jodogne is a Belgian politician known for her role in Brussels regional and local government, particularly in the municipality of Schaerbeek.
  • D. Veerle Poupeye
    Veerle Poupeye is an art historian and curator known for her scholarship and leadership in Caribbean and Jamaican art.
  • E. Martine Robbeets
    Martine Robbeets is a historical linguist known for her work on the relationships among Eurasian language families and for advancing the controversial Transeurasian hypothesis.
  • 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: Dominique Baert
Triple: [Vaudherland, mayor, Dominique Baert]
Generated description
Dominique Baert is a French politician known for serving as a long-time local mayor and member of the National Assembly.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dominique Baert
Target entity description: Dominique Baert is a French politician known for serving as a long-time local mayor and member of the National Assembly.
  • A. Kim Mestdagh
    Kim Mestdagh is a Belgian professional basketball player known as a key shooting guard for Belgium’s national team and for her successful international club career.
  • B. Els Vandevorst
    Els Vandevorst is a Dutch film producer known for her work on acclaimed European and international feature films.
  • C. Cécile Jodogne
    Cécile Jodogne is a Belgian politician known for her role in Brussels regional and local government, particularly in the municipality of Schaerbeek.
  • D. Veerle Poupeye
    Veerle Poupeye is an art historian and curator known for her scholarship and leadership in Caribbean and Jamaican art.
  • E. Martine Robbeets
    Martine Robbeets is a historical linguist known for her work on the relationships among Eurasian language families and for advancing the controversial Transeurasian hypothesis.
  • 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_69d87f2dac988190b74d6e185fa88ba4 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e32dd266c48190991a1484eb2f7bcc completed April 18, 2026, 7:08 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00581c24508190b4888357828fed80 completed May 10, 2026, 10:04 a.m.
NEDg Description generation batch_6a00592562708190ae88f24fb34c7a02 completed May 10, 2026, 10:08 a.m.
NED2 Entity disambiguation (via description) batch_6a005a17fd648190b2c6843f47a9ee2c completed May 10, 2026, 10:12 a.m.
Created at: April 10, 2026, 5:11 a.m.