Triple

T1908684
Position Surface form Disambiguated ID Type / Status
Subject Anna Laetitia Barbauld E38059 entity
Predicate givenName P17 FINISHED
Object Laetitia
Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
 E236777 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: Laetitia | Statement: [Anna Laetitia Barbauld, givenName, Laetitia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Laetitia
Context triple: [Anna Laetitia Barbauld, givenName, Laetitia]
  • A. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • D. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • E. 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.
  • 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: Laetitia
Triple: [Anna Laetitia Barbauld, givenName, Laetitia]
Generated description
Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Laetitia
Target entity description: Laetitia is a feminine given name of Latin origin, historically borne by figures such as the English poet and essayist Anna Laetitia Barbauld.
  • A. Françoise
    Françoise is the given name of Louise de La Vallière, a 17th-century French noblewoman best known as a mistress of King Louis XIV.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • D. Antoinette
    Antoinette is the birth name of Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
  • E. 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.
  • 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_69a8862a26088190aae5243695aeefc0 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1b55edc8190bce8ac97196939a9 completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae5173b2188190bfb492c73a1bba06 completed March 9, 2026, 4:49 a.m.
NEDg Description generation batch_69ae522f0394819087a7e7d9c6ca354c completed March 9, 2026, 4:53 a.m.
NED2 Entity disambiguation (via description) batch_69ae5316acf881908dde9d83c36c8fd0 completed March 9, 2026, 4:56 a.m.
Created at: March 4, 2026, 7:35 p.m.