Triple

T21280678
Position Surface form Disambiguated ID Type / Status
Subject Gina Smith E524512 entity
Predicate givenName P17 FINISHED
Object Gina NE NERFINISHED

How this triple was built (2 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: Gina | Statement: [Gina Smith, givenName, Gina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gina
Context triple: [Gina Smith, givenName, Gina]
  • A. Gina chosen
    Gina is a feminine given name commonly used in English and Italian-speaking countries, often as a short form of names like Regina, Georgina, or Luigina.
  • B. Gina
    Gina is the shy yet principled young woman at the heart of the British television film "The Girl in the Café," whose relationship with a civil servant intertwines personal romance with global political issues.
  • C. Gina Shay
    Gina Shay is an American film producer best known for her work on animated features at DreamWorks Animation, including the hit musical comedy "Trolls."
  • D. Gianna
    Gianna is a feminine given name of Italian origin, often associated with the late Gianna Bryant, daughter of basketball legend Kobe Bryant.
  • E. Jenna
    Jenna is a common feminine given name, often used as a diminutive or variant of Jennifer.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b516293c819089458ea2ec85f85e completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e736d186988190a5b16fcb669ece9f completed April 21, 2026, 8:35 a.m.
Created at: April 16, 2026, 4:02 p.m.