Triple

T598924
Position Surface form Disambiguated ID Type / Status
Subject Theresa May E11449 entity
Predicate givenName P17 FINISHED
Object Theresa
Theresa is a feminine given name of Greek origin, commonly associated in modern times with figures such as former UK Prime Minister Theresa May.
 E75291 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: Theresa | Statement: [Theresa May, givenName, Theresa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Theresa
Context triple: [Theresa May, givenName, Theresa]
  • A. Tory
    Tory refers to a historical British political faction and later party associated with conservatism, monarchy, and traditional institutions, which evolved into the modern Conservative Party.
  • B. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • C. Gillian
    Gillian is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • D. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • E. Emma
    Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
  • 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: Theresa
Triple: [Theresa May, givenName, Theresa]
Generated description
Theresa is a feminine given name of Greek origin, commonly associated in modern times with figures such as former UK Prime Minister Theresa May.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Theresa
Target entity description: Theresa is a feminine given name of Greek origin, commonly associated in modern times with figures such as former UK Prime Minister Theresa May.
  • A. Tory
    Tory refers to a historical British political faction and later party associated with conservatism, monarchy, and traditional institutions, which evolved into the modern Conservative Party.
  • B. Teresa
    Teresa is the religious name of Mother Teresa, the Catholic nun and missionary renowned for her charitable work with the poor in Kolkata, India.
  • C. Gillian
    Gillian is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • D. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking countries.
  • E. Emma
    Emma is a common feminine given name of Germanic origin, widely used in English-speaking and many other countries.
  • 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_69a4932779b881908688590d59c71900 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49d776c6c819081b41a9b55041cd5 completed March 1, 2026, 8:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5216e11248190a8c564a482d649a6 completed March 2, 2026, 5:34 a.m.
NEDg Description generation batch_69a521ce19e08190aadeb913977c2d2e completed March 2, 2026, 5:36 a.m.
NED2 Entity disambiguation (via description) batch_69a5226144f881908e0d1add6be6e156 completed March 2, 2026, 5:38 a.m.
Created at: March 1, 2026, 7:35 p.m.