Triple

T5751931
Position Surface form Disambiguated ID Type / Status
Subject Simu Liu E126872 entity
Predicate portrayed P1668 FINISHED
Object Ken (in Barbie)
Ken (in Barbie) is a charismatic, competitive version of Barbie’s male counterpart as portrayed by Simu Liu in the 2023 live-action film.
 E543897 NE FINISHED

How this triple was built (4 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: Ken (in Barbie) | Statement: [Simu Liu, portrayed, Ken (in Barbie)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ken (in Barbie)
Context triple: [Simu Liu, portrayed, Ken (in Barbie)]
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • C. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • D. Karin
    Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
  • E. Babs
    Babs is a fictional character from the 1970s American sitcom "Blansky's Beauties," which followed the lives of Las Vegas showgirls and their manager.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Ken (in Barbie)
Triple: [Simu Liu, portrayed, Ken (in Barbie)]
Generated description
Ken (in Barbie) is a charismatic, competitive version of Barbie’s male counterpart as portrayed by Simu Liu in the 2023 live-action film.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ken (in Barbie)
Target entity description: Ken (in Barbie) is a charismatic, competitive version of Barbie’s male counterpart as portrayed by Simu Liu in the 2023 live-action film.
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • C. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • D. Karin
    Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
  • E. Babs
    Babs is a fictional character from the 1970s American sitcom "Blansky's Beauties," which followed the lives of Las Vegas showgirls and their manager.
  • F. None of above. chosen

Provenance (5 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_69c00832aedc81909899801b141fa3b4 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c0288b580c81909e1289982b106695 completed March 22, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e3a50b88190a943b2d91d3c5b8e completed March 22, 2026, 11:41 p.m.
NEDg Description generation batch_69c0880ef8608190a602c7b9c7f753fb completed March 23, 2026, 12:23 a.m.
NED2 Entity disambiguation (via description) batch_69c088cff95481908a8e04e763269062 completed March 23, 2026, 12:26 a.m.
Created at: March 22, 2026, 3:48 p.m.