Triple

T198960
Position Surface form Disambiguated ID Type / Status
Subject Barbenheimer phenomenon E4059 entity
Predicate hasPart P35 FINISHED
Object Barbie
Barbie is a 2023 fantasy-comedy film directed by Greta Gerwig that reimagines the iconic Mattel doll in a satirical, self-aware story exploring gender roles, identity, and consumer culture.
 E25542 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: Barbie | Statement: [Barbenheimer phenomenon, hasPart, Barbie]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Barbie
Context triple: [Barbenheimer phenomenon, hasPart, Barbie]
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • C. Nell
    Nell is a feminine given name, often used as a diminutive of names like Eleanor or Helen.
  • D. Chitty-Chitty-Bang-Bang
    Chitty-Chitty-Bang-Bang is a beloved children's story about a magical flying car that inspired a popular film and stage musical.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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: Barbie
Triple: [Barbenheimer phenomenon, hasPart, Barbie]
Generated description
Barbie is a 2023 fantasy-comedy film directed by Greta Gerwig that reimagines the iconic Mattel doll in a satirical, self-aware story exploring gender roles, identity, and consumer culture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Barbie
Target entity description: Barbie is a 2023 fantasy-comedy film directed by Greta Gerwig that reimagines the iconic Mattel doll in a satirical, self-aware story exploring gender roles, identity, and consumer culture.
  • A. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • B. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • C. Nell
    Nell is a feminine given name, often used as a diminutive of names like Eleanor or Helen.
  • D. Chitty-Chitty-Bang-Bang
    Chitty-Chitty-Bang-Bang is a beloved children's story about a magical flying car that inspired a popular film and stage musical.
  • E. Kimberly
    Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
  • 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_69a254bca59881909a15e1496f1508c7 completed Feb. 28, 2026, 2:36 a.m.
NER Named-entity recognition batch_69a25bcb2c7c8190b0e031e93651182a completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a31c93aa348190a7555a8327f7ad99 completed Feb. 28, 2026, 4:49 p.m.
NEDg Description generation batch_69a320abcce08190867d01cd84a0a632 completed Feb. 28, 2026, 5:06 p.m.
NED2 Entity disambiguation (via description) batch_69a321016748819090356a3369138d21 completed Feb. 28, 2026, 5:08 p.m.
Created at: Feb. 28, 2026, 2:44 a.m.