Triple

T2330195
Position Surface form Disambiguated ID Type / Status
Subject Sean Bean E48383 entity
Predicate familyName P18 FINISHED
Object Bean
Bean is a common English surname of Old English origin, associated with various notable individuals including the actor Sean Bean.
 E255978 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: Bean | Statement: [Sean Bean, familyName, Bean]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bean
Context triple: [Sean Bean, familyName, Bean]
  • A. Bean
    Bean is a 1997 British-American comedy film based on Rowan Atkinson’s Mr. Bean character, following his chaotic misadventures in the United States.
  • B. Bean
    Bean is the famous jazz saxophonist Coleman Hawkins, a pioneering tenor sax player whose rich tone and improvisational style helped define early jazz.
  • C. The Bean
    The Bean is a famous stainless steel public sculpture by artist Anish Kapoor located in Chicago’s Millennium Park, renowned for its highly polished, reflective surface and iconic, bean-like shape.
  • D. BEA
    BEA is a U.S. government agency that produces key economic statistics, including measures of national income, output, and growth.
  • E. Jakarta Enterprise Beans
    Jakarta Enterprise Beans is a Jakarta EE server-side component architecture that simplifies the development of transactional, secure, and scalable business logic in Java enterprise applications.
  • 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: Bean
Triple: [Sean Bean, familyName, Bean]
Generated description
Bean is a common English surname of Old English origin, associated with various notable individuals including the actor Sean Bean.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bean
Target entity description: Bean is a common English surname of Old English origin, associated with various notable individuals including the actor Sean Bean.
  • A. Bean
    Bean is a 1997 British-American comedy film based on Rowan Atkinson’s Mr. Bean character, following his chaotic misadventures in the United States.
  • B. Bean
    Bean is the famous jazz saxophonist Coleman Hawkins, a pioneering tenor sax player whose rich tone and improvisational style helped define early jazz.
  • C. The Bean
    The Bean is a famous stainless steel public sculpture by artist Anish Kapoor located in Chicago’s Millennium Park, renowned for its highly polished, reflective surface and iconic, bean-like shape.
  • D. BEA
    BEA is a U.S. government agency that produces key economic statistics, including measures of national income, output, and growth.
  • E. Jakarta Enterprise Beans
    Jakarta Enterprise Beans is a Jakarta EE server-side component architecture that simplifies the development of transactional, secure, and scalable business logic in Java enterprise applications.
  • 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_69a88aa308a88190b0b86c011fda7fce completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc667235c819086140af9db961203 completed March 7, 2026, 6:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae8974ab8c81908ec2bddcc882cf42 completed March 9, 2026, 8:48 a.m.
NEDg Description generation batch_69ae8a084b388190a6d79df8d94b236d completed March 9, 2026, 8:51 a.m.
NED2 Entity disambiguation (via description) batch_69ae8a895b6c8190bfd064742e3cc4f8 completed March 9, 2026, 8:53 a.m.
Created at: March 4, 2026, 7:50 p.m.