Triple

T15968445
Position Surface form Disambiguated ID Type / Status
Subject Count of Loon E387257 entity
Predicate alsoKnownAs P39 FINISHED
Object Comes de Loon
Comes de Loon is the Latin designation for the medieval noble title held by the counts who ruled the County of Loon in present-day Belgium.
 E1186033 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: Comes de Loon | Statement: [Count of Loon, alsoKnownAs, Comes de Loon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Comes de Loon
Context triple: [Count of Loon, alsoKnownAs, Comes de Loon]
  • A. Lulu
    Lulu is a fictional character best known from the Japanese film "Swallowtail Butterfly," in which she is portrayed by actress Ayumi Ito.
  • B. Lulu
    Lulu is an avant-garde opera by Alban Berg, a key work of early 20th-century modernist music associated with the Second Viennese School.
  • C. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • D. Lulu
    Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • E. Lulu
    Lulu is the central character in Harold Pinter’s play "The Birthday Party," around whom the play’s unsettling and ambiguous events revolve.
  • 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: Comes de Loon
Triple: [Count of Loon, alsoKnownAs, Comes de Loon]
Generated description
Comes de Loon is the Latin designation for the medieval noble title held by the counts who ruled the County of Loon in present-day Belgium.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Comes de Loon
Target entity description: Comes de Loon is the Latin designation for the medieval noble title held by the counts who ruled the County of Loon in present-day Belgium.
  • A. Lulu
    Lulu is a fictional character best known from the Japanese film "Swallowtail Butterfly," in which she is portrayed by actress Ayumi Ito.
  • B. Lulu
    Lulu is a common feminine given name or nickname, often used as a diminutive form of names like Louise.
  • C. Lulu
    Lulu is a central character in the 1999 British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • D. Lulu
    Lulu is an avant-garde opera by Alban Berg, a key work of early 20th-century modernist music associated with the Second Viennese School.
  • E. Lulu
    Lulu is the central character in Harold Pinter’s play "The Birthday Party," around whom the play’s unsettling and ambiguous events revolve.
  • 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_69d86da94ccc819083d187f5dc6a123e completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157277e7881908d49f4874766b3b5 completed April 16, 2026, 9:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffbe87149081909ac6129126f597c2 completed May 9, 2026, 11:08 p.m.
NEDg Description generation batch_69ffbf3e80b08190899262a9d03c0e93 completed May 9, 2026, 11:11 p.m.
NED2 Entity disambiguation (via description) batch_69ffbfc0d1548190b7d2e9e10e837f0b completed May 9, 2026, 11:14 p.m.
Created at: April 10, 2026, 4:54 a.m.