Triple

T1584688
Position Surface form Disambiguated ID Type / Status
Subject Mary Therese Winifred Bourke E34037 entity
Predicate givenName P17 FINISHED
Object Therese
Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
 E195946 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: Therese | Statement: [Mary Therese Winifred Bourke, givenName, Therese]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Therese
Context triple: [Mary Therese Winifred Bourke, givenName, Therese]
  • A. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • D. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • E. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • 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: Therese
Triple: [Mary Therese Winifred Bourke, givenName, Therese]
Generated description
Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Therese
Target entity description: Therese is a feminine given name of French origin, commonly associated with Christian saints and used in various European cultures.
  • A. Renée
    Renée is a feminine given name of French origin, commonly used in French-speaking countries and beyond.
  • B. Estelle
    Estelle is a British singer, rapper, and songwriter best known for her hit single "American Boy" featuring Kanye West.
  • C. Marie
    Marie is a widely used European given name, especially common in French-speaking countries, derived from the Hebrew name Miryam (Mary).
  • D. Louise
    Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
  • E. Dorothee
    Dorothee is a feminine given name, commonly used in German- and French-speaking countries, that is a variant of the name Dorothea.
  • 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_69a885fceb2c8190b47e0f7c0aefbff0 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908f240708190a76bb642fc6a6f42 completed March 5, 2026, 4:39 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0c08db88190915a4ca2350c7cc9 completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada1a0510481908ed8c36c9ae9a1a0 completed March 8, 2026, 4:19 p.m.
NED2 Entity disambiguation (via description) batch_69ada26343988190bd067ca97186eb96 completed March 8, 2026, 4:22 p.m.
Created at: March 4, 2026, 7:27 p.m.