Triple

T971472
Position Surface form Disambiguated ID Type / Status
Subject Nanny McPhee E20953 entity
Predicate basedOn P98 FINISHED
Object Nurse Matilda
Nurse Matilda is the magical, stern-yet-kind nanny from Christianna Brand’s children’s books that inspired the film character Nanny McPhee.
 E115200 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: Nurse Matilda | Statement: [Nanny McPhee, basedOn, Nurse Matilda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nurse Matilda
Context triple: [Nanny McPhee, basedOn, Nurse Matilda]
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Martha
    Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
  • C. Lucille
    "Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
  • D. Lucille
    Lucille is the famous black Gibson guitar closely associated with blues legend B.B. King, who named all his guitars by this name.
  • E. Mercy Lewis
    Mercy Lewis was a young servant girl and one of the key accusers during the Salem witch trials of 1692.
  • 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: Nurse Matilda
Triple: [Nanny McPhee, basedOn, Nurse Matilda]
Generated description
Nurse Matilda is the magical, stern-yet-kind nanny from Christianna Brand’s children’s books that inspired the film character Nanny McPhee.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nurse Matilda
Target entity description: Nurse Matilda is the magical, stern-yet-kind nanny from Christianna Brand’s children’s books that inspired the film character Nanny McPhee.
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Martha
    Martha is a feminine given name of Aramaic origin, historically borne by notable figures such as Martha Washington, the first First Lady of the United States.
  • C. Lucille
    "Lucille" is a 1977 country song by Kenny Rogers that became one of his signature hits and a classic of the genre.
  • D. Lucille
    Lucille is the famous black Gibson guitar closely associated with blues legend B.B. King, who named all his guitars by this name.
  • E. Mercy Lewis
    Mercy Lewis was a young servant girl and one of the key accusers during the Salem witch trials of 1692.
  • 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_69a493b33d2c81909c52c369d3ca8436 completed March 1, 2026, 7:29 p.m.
NER Named-entity recognition batch_69a4b44aa6088190a90c44a8f694ec41 completed March 1, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac170a00f481909da0394531ac24fe completed March 7, 2026, 12:16 p.m.
NEDg Description generation batch_69ac18e9be2081909770ab2ead56d0db completed March 7, 2026, 12:24 p.m.
NED2 Entity disambiguation (via description) batch_69ac195b7cd08190b2c3f07d7ae849ed completed March 7, 2026, 12:26 p.m.
Created at: March 1, 2026, 7:40 p.m.