Triple

T15519558
Position Surface form Disambiguated ID Type / Status
Subject Arabella E368923 entity
Predicate hasVariantForm P457 FINISHED
Object Arabelle
Arabelle is a feminine given name, typically considered a variant of Arabella, used in various English-speaking and European cultures.
 E1165886 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: Arabelle | Statement: [Arabella, hasVariantForm, Arabelle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arabelle
Context triple: [Arabella, hasVariantForm, Arabelle]
  • A. Marcelle
    Marcelle is a given name, typically a feminine form of Marcel, used in various cultures.
  • B. Fabienne
    Fabienne is a gentle, somewhat naive French woman who is the devoted fiancée of boxer Butch Coolidge in Quentin Tarantino’s film "Pulp Fiction."
  • C. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • D. Alise Marie
    Alise Marie is a sibling of American actor and musician Carmine Giovinazzo.
  • E. Arlette
    Arlette, also known as Herleva of Falaise, was the mother of William the Conqueror and a key figure in the early life of the first Norman king of England.
  • 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: Arabelle
Triple: [Arabella, hasVariantForm, Arabelle]
Generated description
Arabelle is a feminine given name, typically considered a variant of Arabella, used in various English-speaking and European cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arabelle
Target entity description: Arabelle is a feminine given name, typically considered a variant of Arabella, used in various English-speaking and European cultures.
  • A. Marcelle
    Marcelle is a given name, typically a feminine form of Marcel, used in various cultures.
  • B. Fabienne
    Fabienne is a gentle, somewhat naive French woman who is the devoted fiancée of boxer Butch Coolidge in Quentin Tarantino’s film "Pulp Fiction."
  • C. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • D. Alise Marie
    Alise Marie is a sibling of American actor and musician Carmine Giovinazzo.
  • E. Arlette
    Arlette, also known as Herleva of Falaise, was the mother of William the Conqueror and a key figure in the early life of the first Norman king of England.
  • 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_69d85a1794cc8190b0b428716296e63e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e040343d9c8190a7d1f197c108bd9d completed April 16, 2026, 1:49 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56bb23e88190bb3e9ad5e409a2f8 completed May 9, 2026, 3:46 p.m.
NEDg Description generation batch_69ff57e4dc108190b18e801a814a7c11 completed May 9, 2026, 3:51 p.m.
NED2 Entity disambiguation (via description) batch_69ff583680588190b52a75a73624503a completed May 9, 2026, 3:52 p.m.
Created at: April 10, 2026, 4:04 a.m.