Triple

T14934379
Position Surface form Disambiguated ID Type / Status
Subject George E372350 entity
Predicate hasFeminineForm P1613 FINISHED
Object Georgina E472597 NE FINISHED

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: Georgina | Statement: [George, hasFeminineForm, Georgina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Georgina
Context triple: [George, hasFeminineForm, Georgina]
  • A. Georgina chosen
    Georgina is a feminine given name used in various English-speaking and European countries, often considered a variant of Georgia or the feminine form of George.
  • B. Georgina
    Georgina is a lakeside town in Ontario, Canada, known for its recreational waterfront communities and proximity to Lake Simcoe.
  • C. Georgina Hale
    Georgina Hale is a British actress known for her work in film and television, particularly in character-driven and often offbeat roles.
  • D. Geraldine
    Geraldine is a small rural service town in the South Island of New Zealand, known for its scenic surroundings and role as a gateway to the Canterbury high country.
  • E. Geraldine
    Geraldine is a feminine given name of Germanic origin that has been borne by various notable figures, including actress Geraldine Chaplin.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded646a0808190ba5c0c91bde011c5 completed April 15, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe968bbbac8190a258c42b226f9def completed May 9, 2026, 2:06 a.m.
Created at: April 10, 2026, 2:37 a.m.