Triple

T16580670
Position Surface form Disambiguated ID Type / Status
Subject Arline Judge E402820 entity
Predicate givenName P17 FINISHED
Object Arline
Arline is a feminine given name, often used in English-speaking countries and associated with several notable women in the arts and entertainment.
 E1221537 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: Arline | Statement: [Arline Judge, givenName, Arline]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arline
Context triple: [Arline Judge, givenName, Arline]
  • A. Earline
    Earline is a character in Ishmael Reed's satirical novel "Mumbo Jumbo," which explores themes of African American culture, history, and resistance.
  • B. Harline
    Harline is a surname most notably associated with Leigh Harline, an American film composer known for his work with Walt Disney Studios.
  • C. Arletta
    Arletta, better known as Herleva of Falaise, was the mother of William the Conqueror and a notable figure in 11th-century Norman history.
  • D. Arley
    Arley is a small rural town located in Winston County in the U.S. state of Alabama.
  • E. Angeline
    Angeline is the given name of American actress Angie Dickinson, known for her roles in film and television from the 1950s onward.
  • 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: Arline
Triple: [Arline Judge, givenName, Arline]
Generated description
Arline is a feminine given name, often used in English-speaking countries and associated with several notable women in the arts and entertainment.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arline
Target entity description: Arline is a feminine given name, often used in English-speaking countries and associated with several notable women in the arts and entertainment.
  • A. Earline
    Earline is a character in Ishmael Reed's satirical novel "Mumbo Jumbo," which explores themes of African American culture, history, and resistance.
  • B. Harline
    Harline is a surname most notably associated with Leigh Harline, an American film composer known for his work with Walt Disney Studios.
  • C. Arletta
    Arletta, better known as Herleva of Falaise, was the mother of William the Conqueror and a notable figure in 11th-century Norman history.
  • D. Arley
    Arley is a small rural town located in Winston County in the U.S. state of Alabama.
  • E. Angeline
    Angeline is the given name of American actress Angie Dickinson, known for her roles in film and television from the 1950s onward.
  • 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_69d88387363c8190a97a0c942130de97 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35960834c819080eed0c9f32b881d completed April 18, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a006ef0bc4c8190b06b03c06d344abf completed May 10, 2026, 11:41 a.m.
NEDg Description generation batch_6a006ff5bdb88190be90d7446e24b61f completed May 10, 2026, 11:45 a.m.
NED2 Entity disambiguation (via description) batch_6a007088fd988190b3dfef081769d03e completed May 10, 2026, 11:48 a.m.
Created at: April 10, 2026, 5:16 a.m.