Triple

T2887000
Position Surface form Disambiguated ID Type / Status
Subject Charlotte Greenwood E59529 entity
Predicate name P16 FINISHED
Object Charlotte Greenwood E59529 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: Charlotte Greenwood | Statement: [Charlotte Greenwood, name, Charlotte Greenwood]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charlotte Greenwood
Context triple: [Charlotte Greenwood, name, Charlotte Greenwood]
  • A. Charlotte Greenwood chosen
    Charlotte Greenwood was an American actress, comedian, and dancer best known for her lanky physical comedy and memorable supporting roles in stage and film musicals.
  • B. Charlotte York
    Charlotte York is a prim, romantic, and traditional art dealer and one of the four central female protagonists in the Sex and the City franchise.
  • C. Sarah Allerton
    Sarah Allerton was an early 17th-century Englishwoman known primarily as the wife of Mayflower passenger Degory Priest and a member of the broader Pilgrim community.
  • D. Charlotte Hennessy
    Charlotte Hennessy was the mother of silent film star Mary Pickford and played a key role in managing and supporting her early acting career.
  • E. Madeline Ashton
    Madeline Ashton is a vain, aging Hollywood actress whose obsession with youth and beauty leads her to drink a magical potion granting eternal life in the dark comedy film "Death Becomes Her."
  • 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_69ab4ac739188190a112f42a5a69c951 completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abe047aa7c8190a0ed570c13f3a1a2 completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b031713e14819098db4cfaaea74f73 completed March 10, 2026, 2:57 p.m.
Created at: March 6, 2026, 10:03 p.m.