Triple

T761303
Position Surface form Disambiguated ID Type / Status
Subject Maria Cotton Mather E16074 entity
Predicate givenName P17 FINISHED
Object Maria
Maria is a female given name of Latin origin meaning "beloved" or "wished-for child," widely used across many cultures and languages.
 E103006 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: Maria | Statement: [Maria Cotton Mather, givenName, Maria]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Maria
Context triple: [Maria Cotton Mather, givenName, Maria]
  • A. Maria
    Maria is an alternate given name of Letizia Ramolino, the mother of Napoleon Bonaparte and a notable figure in Corsican and French history.
  • B. Maria
    Maria is the birth name of Marie Curie, the pioneering physicist and chemist who conducted groundbreaking research on radioactivity.
  • C. Mary
    Mary is a central figure in Christianity, venerated as the mother of Jesus and often honored as the Virgin Mary.
  • D. Mary
    Mary, Princess Royal and Countess of Harewood, was a daughter of King George V and Queen Mary of the United Kingdom and a prominent British royal figure in the early to mid-20th century.
  • E. Mary
    Mary is the given first name of the acclaimed American actress Meryl Streep.
  • 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: Maria
Triple: [Maria Cotton Mather, givenName, Maria]
Generated description
Maria is a female given name of Latin origin meaning "beloved" or "wished-for child," widely used across many cultures and languages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Maria
Target entity description: Maria is a female given name of Latin origin meaning "beloved" or "wished-for child," widely used across many cultures and languages.
  • A. Maria
    Maria is an alternate given name of Letizia Ramolino, the mother of Napoleon Bonaparte and a notable figure in Corsican and French history.
  • B. Maria
    Maria is the birth name of Marie Curie, the pioneering physicist and chemist who conducted groundbreaking research on radioactivity.
  • C. Mary
    Mary, Princess Royal and Countess of Harewood, was a daughter of King George V and Queen Mary of the United Kingdom and a prominent British royal figure in the early to mid-20th century.
  • D. Mary
    Mary is a significant urban and economic center in southeastern Turkmenistan, known for its role in the country’s natural gas and cotton industries.
  • E. Mary
    Mary is the given first name of Margaret Truman, the daughter of U.S. President Harry S. Truman and a noted author and singer.
  • 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_69a493684ee48190bd43b7c78da4aec8 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a682e7d081909c9cd7839a49fb0b completed March 1, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b83948b48190af0349dd73ec3951 completed March 4, 2026, 4:42 a.m.
NEDg Description generation batch_69a7b99c19fc819090e0f9a042a8c12c completed March 4, 2026, 4:48 a.m.
NED2 Entity disambiguation (via description) batch_69a7ba44b79c8190b0ce8a430fe928e5 completed March 4, 2026, 4:51 a.m.
Created at: March 1, 2026, 7:37 p.m.