Triple

T2892318
Position Surface form Disambiguated ID Type / Status
Subject Rooney Mara E63855 entity
Predicate notableWork P4 FINISHED
Object Carol
Carol is a critically acclaimed 2015 romantic drama film, directed by Todd Haynes and starring Cate Blanchett and Rooney Mara, about a forbidden love affair between two women in 1950s New York.
 E307551 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: Carol | Statement: [Rooney Mara, notableWork, Carol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Carol
Context triple: [Rooney Mara, notableWork, Carol]
  • A. Carol
    Carol is a feminine given name commonly used in English-speaking countries, often associated with figures in entertainment and literature.
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • D. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • E. Nancy
    Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
  • 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: Carol
Triple: [Rooney Mara, notableWork, Carol]
Generated description
Carol is a critically acclaimed 2015 romantic drama film, directed by Todd Haynes and starring Cate Blanchett and Rooney Mara, about a forbidden love affair between two women in 1950s New York.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Carol
Target entity description: Carol is a critically acclaimed 2015 romantic drama film, directed by Todd Haynes and starring Cate Blanchett and Rooney Mara, about a forbidden love affair between two women in 1950s New York.
  • A. Carol
    Carol is a feminine given name commonly used in English-speaking countries, often associated with figures in entertainment and literature.
  • B. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • C. Barbara
    Barbara is a station on Paris Métro Line 4 serving the southern suburbs of the French capital.
  • D. Nancy
    Nancy is a feminine given name of Hebrew origin meaning "grace" that became especially popular in English-speaking countries in the 20th century.
  • E. Nancy
    Nancy is a historic city in northeastern France renowned for its elegant 18th-century architecture and UNESCO-listed Place Stanislas.
  • 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_69ab4c45822c8190830c5f2bb97bcfd0 completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe060f49c8190bc804614a141c738 completed March 7, 2026, 8:22 a.m.
NED1 Entity disambiguation (via context triple) batch_69b0317e15248190bade0f0fd930581a completed March 10, 2026, 2:58 p.m.
NEDg Description generation batch_69b034b304f8819096eda16e314912b4 completed March 10, 2026, 3:11 p.m.
NED2 Entity disambiguation (via description) batch_69b03c60a5988190b015a1cf05068845 completed March 10, 2026, 3:44 p.m.
Created at: March 6, 2026, 10:07 p.m.