Triple

T404116
Position Surface form Disambiguated ID Type / Status
Subject Christina Aguilera E9347 entity
Predicate givenName P17 FINISHED
Object Christina
Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
 E75185 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: Christina | Statement: [Christina Aguilera, givenName, Christina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Christina
Context triple: [Christina Aguilera, givenName, Christina]
  • A. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • B. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • C. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • D. Roberta
    Roberta is a feminine given name commonly used in various languages, derived from the masculine name Robert.
  • E. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking 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: Christina
Triple: [Christina Aguilera, givenName, Christina]
Generated description
Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Christina
Target entity description: Christina is a feminine given name widely used in many cultures, often associated with notable figures in entertainment, arts, and public life.
  • A. Paula
    Paula is a feminine given name used in many languages, derived from the Latin name Paulus meaning "small" or "humble."
  • B. Cynthia
    Cynthia is a common feminine given name used in various cultures, often associated with the Greek moon goddess Artemis.
  • C. Joanna
    Joanna is the first name of Joanna Newsom, an American harpist, singer-songwriter, and musician known for her intricate compositions and distinctive vocal style.
  • D. Roberta
    Roberta is a feminine given name commonly used in various languages, derived from the masculine name Robert.
  • E. Kathleen
    Kathleen is a feminine given name of Irish origin, derived from the name Catherine and widely used in English-speaking 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2eca226fc81909d6ccc38a637daa6 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5216a74088190bf8d363d32952c28 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: Feb. 28, 2026, 1:08 p.m.