Triple

T10201975
Position Surface form Disambiguated ID Type / Status
Subject Red 2 E238903 entity
Predicate hasCharacter P2308 FINISHED
Object Sarah Ross
Sarah Ross is a retired CIA analyst drawn back into the world of espionage and action in the comedy-action film "Red 2."
 E848096 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: Sarah Ross | Statement: [Red 2, hasCharacter, Sarah Ross]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sarah Ross
Context triple: [Red 2, hasCharacter, Sarah Ross]
  • A. Rose Ross
    Rose Ross is a central character in the 2015 Western film "Slow West," around whom much of the story’s journey and conflict revolve.
  • B. Charlotte Ross
    Charlotte Ross is an American actress best known for her television roles on series such as NYPD Blue, Days of Our Lives, and Glee.
  • C. Sarah Kirkpatrick
    Sarah Kirkpatrick was the wife of prominent 18th-century American Presbyterian minister and educator Samuel Davies.
  • D. Mary Brian Stapler Ross
    Mary Brian Stapler Ross was the wife of American Revolutionary War figure John Ross and a member of Philadelphia’s prominent Stapler family.
  • E. Caroline Ross
    Caroline Ross is a film editor known for her work on the science fiction movie "Starship Troopers."
  • 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: Sarah Ross
Triple: [Red 2, hasCharacter, Sarah Ross]
Generated description
Sarah Ross is a retired CIA analyst drawn back into the world of espionage and action in the comedy-action film "Red 2."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sarah Ross
Target entity description: Sarah Ross is a retired CIA analyst drawn back into the world of espionage and action in the comedy-action film "Red 2."
  • A. Rose Ross
    Rose Ross is a central character in the 2015 Western film "Slow West," around whom much of the story’s journey and conflict revolve.
  • B. Charlotte Ross
    Charlotte Ross is an American actress best known for her television roles on series such as NYPD Blue, Days of Our Lives, and Glee.
  • C. Sarah Kirkpatrick
    Sarah Kirkpatrick was the wife of prominent 18th-century American Presbyterian minister and educator Samuel Davies.
  • D. Mary Brian Stapler Ross
    Mary Brian Stapler Ross was the wife of American Revolutionary War figure John Ross and a member of Philadelphia’s prominent Stapler family.
  • E. Caroline Ross
    Caroline Ross is a film editor known for her work on the science fiction movie "Starship Troopers."
  • 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_69ca84e1ea088190b38162e43d4cfa8f completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdee40cb7481908a1bf4d5636eb8ef completed April 2, 2026, 4:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69d32afa75ec8190bbaf2e69b4ee24f1 completed April 6, 2026, 3:39 a.m.
NEDg Description generation batch_69d32c4365448190a8cef4d78c340441 completed April 6, 2026, 3:45 a.m.
NED2 Entity disambiguation (via description) batch_69d3302842188190ad4e91172e9d2391 completed April 6, 2026, 4:01 a.m.
Created at: March 30, 2026, 9:14 p.m.