Triple

T11009020
Position Surface form Disambiguated ID Type / Status
Subject You'll Never Get Rich E260198 entity
Predicate hasCastMember P2308 FINISHED
Object Osa Massen
Osa Massen was a Danish-American actress known for her roles in Hollywood films of the 1930s and 1940s, often portraying sophisticated or mysterious women.
E899371 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: Osa Massen | Statement: [You'll Never Get Rich, hasCastMember, Osa Massen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Osa Massen
Context triple: [You'll Never Get Rich, hasCastMember, Osa Massen]
  • A. Massi
    Massi is a common Italian diminutive or nickname for the given name Massimiliano.
  • B. Klauder
    Klauder is a family surname most notably associated with individuals such as physicist John R. Klauder.
  • C. Masa
    Masa is a Central Chadic language spoken primarily in parts of Cameroon and Chad.
  • D. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • E. Matta
    Matta is a surname most prominently associated with Thad Matta, a successful American college basketball coach known for his tenures at Xavier and Ohio State.
  • 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: Osa Massen
Triple: [You'll Never Get Rich, hasCastMember, Osa Massen]
Generated description
Osa Massen was a Danish-American actress known for her roles in Hollywood films of the 1930s and 1940s, often portraying sophisticated or mysterious women.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Osa Massen
Target entity description: Osa Massen was a Danish-American actress known for her roles in Hollywood films of the 1930s and 1940s, often portraying sophisticated or mysterious women.
  • A. Massi
    Massi is a common Italian diminutive or nickname for the given name Massimiliano.
  • B. Klauder
    Klauder is a family surname most notably associated with individuals such as physicist John R. Klauder.
  • C. Masa
    Masa is a Central Chadic language spoken primarily in parts of Cameroon and Chad.
  • D. Molinaro
    Molinaro is an Italian occupational surname, historically associated with millers and derived from the same root as "Molinero."
  • E. Matta
    Matta is a surname most prominently associated with Thad Matta, a successful American college basketball coach known for his tenures at Xavier and Ohio State.
  • 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_69d6aa9687448190b28d353b1b6a610e completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7978810208190b8e2966ae67b6314 completed April 9, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69e37498b9fc8190860acede4f49ea4a completed April 18, 2026, 12:10 p.m.
NEDg Description generation batch_69e378dcc92c8190952d4acfee2a309c completed April 18, 2026, 12:28 p.m.
NED2 Entity disambiguation (via description) batch_69e37be75a588190abb9569ef1e87279 completed April 18, 2026, 12:41 p.m.
Created at: April 8, 2026, 9:25 p.m.