Triple

T8891282
Position Surface form Disambiguated ID Type / Status
Subject Crocker E211680 entity
Predicate hasNotableBearer P458 FINISHED
Object Susan Crocker
Susan Crocker is a notable individual who shares the Crocker surname, likely recognized for contributions in her professional or public life.
E896110 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: Susan Crocker | Statement: [Crocker, hasNotableBearer, Susan Crocker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susan Crocker
Context triple: [Crocker, hasNotableBearer, Susan Crocker]
  • A. Sally Crocker
    Sally Crocker is a notable individual who shares the Crocker surname, recognized enough to be specifically cited as a bearer of the name.
  • B. Patricia Crocker
    Patricia Crocker is a notable individual recognized as a prominent bearer of the Crocker surname.
  • C. Margaret Crocker
    Margaret Crocker was a 19th-century Sacramento philanthropist best known for donating her late husband's extensive art collection and endowing what became the Crocker Art Museum.
  • D. Jane Brucker
    Jane Brucker is an American actress best known for playing Lisa Houseman, the protagonist’s older sister, in the classic 1987 film "Dirty Dancing."
  • E. Laura Bickford
    Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
  • 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: Susan Crocker
Triple: [Crocker, hasNotableBearer, Susan Crocker]
Generated description
Susan Crocker is a notable individual who shares the Crocker surname, likely recognized for contributions in her professional or public life.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Susan Crocker
Target entity description: Susan Crocker is a notable individual who shares the Crocker surname, likely recognized for contributions in her professional or public life.
  • A. Sally Crocker
    Sally Crocker is a notable individual who shares the Crocker surname, recognized enough to be specifically cited as a bearer of the name.
  • B. Patricia Crocker
    Patricia Crocker is a notable individual recognized as a prominent bearer of the Crocker surname.
  • C. Margaret Crocker
    Margaret Crocker was a 19th-century Sacramento philanthropist best known for donating her late husband's extensive art collection and endowing what became the Crocker Art Museum.
  • D. Jane Brucker
    Jane Brucker is an American actress best known for playing Lisa Houseman, the protagonist’s older sister, in the classic 1987 film "Dirty Dancing."
  • E. Laura Bickford
    Laura Bickford is an American film producer best known for her work on acclaimed independent and studio films, including the Oscar-winning drama "Traffic."
  • 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_69ca83907954819096d52a245b635841 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc61ba33c48190a657fc4147a326c0 completed April 1, 2026, 12:07 a.m.
NED1 Entity disambiguation (via context triple) batch_69e23aeb6f9c8190a986af35bcf353f7 completed April 17, 2026, 1:51 p.m.
NEDg Description generation batch_69e2453f6f008190847298f4006290f7 completed April 17, 2026, 2:35 p.m.
NED2 Entity disambiguation (via description) batch_69e288b1d64c8190b31313634b706d0a completed April 17, 2026, 7:23 p.m.
Created at: March 30, 2026, 6:54 p.m.