Triple

T6882415
Position Surface form Disambiguated ID Type / Status
Subject Camilla Parker Bowles E158829 entity
Predicate child P120 FINISHED
Object Laura Lopes E175020 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: Laura Lopes | Statement: [Camilla Parker Bowles, child, Laura Lopes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Laura Lopes
Context triple: [Camilla Parker Bowles, child, Laura Lopes]
  • A. Laura Lopes chosen
    Laura Lopes is a British art curator and gallery co-founder, best known as the daughter of Queen Camilla and her first husband, Andrew Parker Bowles.
  • B. Lais Ribeiro
    Lais Ribeiro is a Brazilian fashion model best known for her work with Victoria’s Secret and appearances in its high-profile runway shows.
  • C. Sara Sampaio
    Sara Sampaio is a Portuguese fashion model best known for her work with Victoria’s Secret and appearances in major international fashion magazines and campaigns.
  • D. Caroline Celico
    Caroline Celico is a Brazilian socialite, former pastor, and philanthropist known for her work with charitable organizations and her past marriage to footballer Kaká.
  • E. Helen Santos
    Helen Santos is a fictional character in "The West Wing," known as the supportive and politically savvy wife of presidential candidate Matt Santos.
  • 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_69c688342f6c8190ad7eea6ba262db99 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d8e90c9481908d00634f67fa71f8 completed March 27, 2026, 7:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7584189008190b908f530a4525885 completed March 28, 2026, 4:25 a.m.
Created at: March 27, 2026, 2:23 p.m.