Triple

T10066352
Position Surface form Disambiguated ID Type / Status
Subject David Lauren E213111 entity
Predicate familyName P18 FINISHED
Object Lauren E478716 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: Lauren | Statement: [David Lauren, familyName, Lauren]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lauren
Context triple: [David Lauren, familyName, Lauren]
  • A. Lauren
    Lauren is a central female protagonist in the romantic comedy film "Think Like a Man," portrayed as a successful, relationship-seeking woman whose love life is influenced by Steve Harvey’s dating advice.
  • B. Lauren
    Lauren is a central character in the musical "Kinky Boots," known as a quirky, down-to-earth factory worker who becomes a key ally and love interest to the protagonist.
  • C. Lauren chosen
    Lauren is a common given name used for people of any gender in various English-speaking and other countries.
  • D. Lauren Lane
    Lauren Lane is an American television and stage actress best known for playing the sophisticated and sarcastic C.C. Babcock on the 1990s sitcom "The Nanny."
  • E. Lauren Barnes
    Lauren Barnes is an American professional soccer defender best known for her standout career with OL Reign in the National Women's Soccer League.
  • 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_69ca83977128819084084eb7d1d8c52a completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdcff63a4c8190bb08a0428aafa189 completed April 2, 2026, 2:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cbb441388190bf01925b8624377d completed April 5, 2026, 8:53 p.m.
Created at: March 30, 2026, 8:58 p.m.