Triple

T17825952
Position Surface form Disambiguated ID Type / Status
Subject Berenguer E445117 entity
Predicate hasVariant P455 FINISHED
Object Berenger
Berenger is a masculine given name and surname of Germanic origin, commonly found in various European cultures with multiple spelling variants.
 E1290875 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: Berenger | Statement: [Berenguer, hasVariant, Berenger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Berenger
Context triple: [Berenguer, hasVariant, Berenger]
  • A. Antoine Hastoy
    Antoine Hastoy is a French rugby union fly-half known for his playmaking skills in the Top 14 and appearances with the French national team.
  • B. Bivin of Gorze
    Bivin of Gorze was a 9th-century Frankish nobleman and courtier who became a key progenitor of the influential Bosonid dynasty in the Carolingian realm.
  • C. Eynard
    Eynard was a notable Philhellene known for his support of the Greek cause during the Greek War of Independence.
  • D. Manceau
    Manceau is the French term used to refer to an inhabitant or native of the city of Le Mans.
  • E. Xavier de Guillebon
    Xavier de Guillebon is a French actor known for his supporting roles in film and television, including appearances in international productions.
  • 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: Berenger
Triple: [Berenguer, hasVariant, Berenger]
Generated description
Berenger is a masculine given name and surname of Germanic origin, commonly found in various European cultures with multiple spelling variants.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Berenger
Target entity description: Berenger is a masculine given name and surname of Germanic origin, commonly found in various European cultures with multiple spelling variants.
  • A. Antoine Hastoy
    Antoine Hastoy is a French rugby union fly-half known for his playmaking skills in the Top 14 and appearances with the French national team.
  • B. Bivin of Gorze
    Bivin of Gorze was a 9th-century Frankish nobleman and courtier who became a key progenitor of the influential Bosonid dynasty in the Carolingian realm.
  • C. Eynard
    Eynard was a notable Philhellene known for his support of the Greek cause during the Greek War of Independence.
  • D. Manceau
    Manceau is the French term used to refer to an inhabitant or native of the city of Le Mans.
  • E. Xavier de Guillebon
    Xavier de Guillebon is a French actor known for his supporting roles in film and television, including appearances in international productions.
  • 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_69d8b9f0de78819099395b14db75a8a6 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48914226c819083edcc78e00b2d42 completed April 19, 2026, 7:49 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0306f1f3908190b51533fb9bc54430 completed May 12, 2026, 10:54 a.m.
NEDg Description generation batch_6a0307a17a4c81909a0b19b85a2d0adb completed May 12, 2026, 10:57 a.m.
NED2 Entity disambiguation (via description) batch_6a03080d46fc8190890000cfd0ede60e completed May 12, 2026, 10:59 a.m.
Created at: April 10, 2026, 10:15 a.m.