Triple

T56602
Position Surface form Disambiguated ID Type / Status
Subject Norman E1119 entity
Predicate hasFeminineForm P1613 FINISHED
Object Norma
Norma is a feminine given name used in various cultures, often considered the female counterpart of the name Norman.
E12662 NE FINISHED

How this triple was built (5 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: Norma | Statement: [Norman, hasFeminineForm, Norma]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norma
Context triple: [Norman, hasFeminineForm, Norma]
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Nance
    Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • C. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • D. Angela
    Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
  • E. Lucille Sheardown
    Lucille Sheardown was one of the later wives of American inventor Lee de Forest, associated with his personal life rather than his pioneering work in radio and electronics.
  • 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: Norma
Triple: [Norman, hasFeminineForm, Norma]
Generated description
Norma is a feminine given name used in various cultures, often considered the female counterpart of the name Norman.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Norma
Target entity description: Norma is a feminine given name used in various cultures, often considered the female counterpart of the name Norman.
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Nance
    Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
  • C. Rita
    Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
  • D. Angela
    Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
  • E. Lucille Sheardown
    Lucille Sheardown was one of the later wives of American inventor Lee de Forest, associated with his personal life rather than his pioneering work in radio and electronics.
  • F. None of above. chosen
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasFeminineForm
Context triple: [Norman, hasFeminineForm, Norma]
  • A. hasFemaleEquivalent chosen
    Indicates that one entity serves as the female counterpart or equivalent of another entity.
  • B. hasNumberOfGenders
    Indicates the relationship that specifies how many distinct genders are associated with or recognized for a given entity.
  • C. hasFullForm
    Indicates that one entity is the complete, expanded, or unabbreviated form of another entity.
  • D. hasGenderedTitle
    Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
  • E. hasDiminutive
    Indicates that one entity is a diminutive form or smaller/affectionate variant of another entity.
  • F. None of above.

Provenance (6 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_69a248adc5b48190aa8db9fb092fb28a completed Feb. 28, 2026, 1:45 a.m.
NER Named-entity recognition batch_69a24b915c9881908c798f4dacb39f1d completed Feb. 28, 2026, 1:57 a.m.
NED1 Entity disambiguation (via context triple) batch_69a29176de5c819086e1cfec0a23d9d7 completed Feb. 28, 2026, 6:55 a.m.
NEDg Description generation batch_69a291e8a54081909f9377d7decca7d0 completed Feb. 28, 2026, 6:57 a.m.
NED2 Entity disambiguation (via description) batch_69a2927e410c81909879207d8b25a895 completed Feb. 28, 2026, 7 a.m.
PD Predicate disambiguation batch_69a24ac6799c8190b508933acc0a4c7d completed Feb. 28, 2026, 1:54 a.m.
Created at: Feb. 28, 2026, 1:50 a.m.