Triple

T12524574
Position Surface form Disambiguated ID Type / Status
Subject Fernanda Montenegro E299402 entity
Predicate givenName P17 FINISHED
Object Arlette
Arlette is the given first name of renowned Brazilian actress Fernanda Montenegro, a leading figure in Brazilian theater, film, and television.
 E999516 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: Arlette | Statement: [Fernanda Montenegro, givenName, Arlette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Arlette
Context triple: [Fernanda Montenegro, givenName, Arlette]
  • A. 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.
  • B. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • C. Antoinette
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • D. Martine
    Martine is a feminine given name commonly used in French- and English-speaking countries.
  • E. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • 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: Arlette
Triple: [Fernanda Montenegro, givenName, Arlette]
Generated description
Arlette is the given first name of renowned Brazilian actress Fernanda Montenegro, a leading figure in Brazilian theater, film, and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Arlette
Target entity description: Arlette is the given first name of renowned Brazilian actress Fernanda Montenegro, a leading figure in Brazilian theater, film, and television.
  • A. 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.
  • B. Antoinette
    Antoinette is a feminine given name of French origin, historically associated with nobility and later borne by various notable figures in the arts and public life.
  • C. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • D. Martine
    Martine is a feminine given name commonly used in French- and English-speaking countries.
  • E. Aline
    Aline is a feminine given name of French origin, commonly used in various cultures and languages.
  • 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9545c2aa081908e8a5a94d30e23eb completed April 10, 2026, 7:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f67c6a590881908b0fe779f3698ea2 completed May 2, 2026, 10:36 p.m.
NEDg Description generation batch_69f67d64ed3481908d434c20796866f9 completed May 2, 2026, 10:40 p.m.
NED2 Entity disambiguation (via description) batch_69f67e82e35081909c4b5fad7e941610 completed May 2, 2026, 10:45 p.m.
Created at: April 8, 2026, 9:57 p.m.