Triple

T7806340
Position Surface form Disambiguated ID Type / Status
Subject Lawrence E180561 entity
Predicate hasVariantForm P457 FINISHED
Object Laurence E48169 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: Laurence | Statement: [Lawrence, hasVariantForm, Laurence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laurence
Context triple: [Lawrence, hasVariantForm, Laurence]
  • A. Laurence chosen
    Laurence is a masculine given name of Latin origin, commonly used in English-speaking countries.
  • B. Theodore "Laurie" Laurence
    Theodore "Laurie" Laurence is a charming, wealthy young man and close friend of the March family in Louisa May Alcott's novel "Little Women," who ultimately marries Amy March.
  • C. Laurence Boone
    Laurence Boone is a French economist and diplomat who serves as France’s ambassador to the United States.
  • D. Reginald
    Reginald is a masculine given name of English origin that has been borne by various notable figures, including military officers, politicians, and artists.
  • E. Leonard
    Leonard is a masculine given name of Germanic origin, commonly used in English-speaking countries and borne by numerous notable figures in arts, sports, and public life.
  • 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_69ca827e50cc8190a92a733577184938 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69caf639fba88190a7c117aab19c0b0f completed March 30, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb1450a2408190afe0459086d4480f completed March 31, 2026, 12:24 a.m.
Created at: March 30, 2026, 4:35 p.m.