Triple

T17213974
Position Surface form Disambiguated ID Type / Status
Subject American Factory E417804 entity
Predicate producer P490 FINISHED
Object Maren Grainger-Monsen
Maren Grainger-Monsen is an American physician and documentary filmmaker known for her work on ethically focused and socially engaged films.
E1257855 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: Maren Grainger-Monsen | Statement: [American Factory, producer, Maren Grainger-Monsen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Maren Grainger-Monsen
Context triple: [American Factory, producer, Maren Grainger-Monsen]
  • A. Sigrid Horne-Rasmussen
    Sigrid Horne-Rasmussen was a Danish actress known for her work in mid-20th-century Danish cinema and theatre.
  • B. Sonja Haraldsen
    Sonja Haraldsen, now Queen Sonja of Norway, is the queen consort of King Harald V and a prominent member of the Norwegian royal family known for her cultural and charitable work.
  • C. Ingrid Knudsen
    Ingrid Knudsen is the woman targeted by a violent cult and safeguarded by tough cop Marion “Cobra” Cobretti in the 1986 action film "Cobra."
  • D. Maren Svarstad
    Maren Svarstad was a daughter of the Norwegian Nobel Prize–winning author Sigrid Undset.
  • E. Nina Grieg
    Nina Grieg was a Norwegian lyric soprano and the wife and frequent musical collaborator of composer Edvard Grieg.
  • 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: Maren Grainger-Monsen
Triple: [American Factory, producer, Maren Grainger-Monsen]
Generated description
Maren Grainger-Monsen is an American physician and documentary filmmaker known for her work on ethically focused and socially engaged films.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Maren Grainger-Monsen
Target entity description: Maren Grainger-Monsen is an American physician and documentary filmmaker known for her work on ethically focused and socially engaged films.
  • A. Sigrid Horne-Rasmussen
    Sigrid Horne-Rasmussen was a Danish actress known for her work in mid-20th-century Danish cinema and theatre.
  • B. Sonja Haraldsen
    Sonja Haraldsen, now Queen Sonja of Norway, is the queen consort of King Harald V and a prominent member of the Norwegian royal family known for her cultural and charitable work.
  • C. Ingrid Knudsen
    Ingrid Knudsen is the woman targeted by a violent cult and safeguarded by tough cop Marion “Cobra” Cobretti in the 1986 action film "Cobra."
  • D. Maren Svarstad
    Maren Svarstad was a daughter of the Norwegian Nobel Prize–winning author Sigrid Undset.
  • E. Nina Grieg
    Nina Grieg was a Norwegian lyric soprano and the wife and frequent musical collaborator of composer Edvard Grieg.
  • 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_69d886d779488190b131369541c04e7d completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42dc795d08190b90801a4f8b23afe completed April 19, 2026, 1:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a016751a5788190a385774d1ff002d0 completed May 11, 2026, 5:21 a.m.
NEDg Description generation batch_6a016846e8248190b9209bb344396bfa completed May 11, 2026, 5:25 a.m.
NED2 Entity disambiguation (via description) batch_6a0168b535a88190b8c84c76871ae2fe completed May 11, 2026, 5:27 a.m.
Created at: April 10, 2026, 5:38 a.m.