Triple

T4750701
Position Surface form Disambiguated ID Type / Status
Subject The Losers (2010 film) E105469 entity
Predicate mainCharacter P1183 FINISHED
Object Jensen
Jensen is the wisecracking, tech-savvy hacker and communications expert on the black-ops team in the action film "The Losers" (2010).
 E467290 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: Jensen | Statement: [The Losers (2010 film), mainCharacter, Jensen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jensen
Context triple: [The Losers (2010 film), mainCharacter, Jensen]
  • A. Jensen
    Jensen is a key crew member aboard the Cloverfield space station in the science fiction horror film "The Cloverfield Paradox," whose actions and fate are central to the movie’s interdimensional crisis.
  • B. Jensen
    Jensen is a Scandinavian-origin surname and given name, most commonly associated with Danish and Norwegian patronymic naming traditions.
  • C. Jenson
    Jenson is a given name and surname of English origin, commonly used as a variant spelling of Jensen.
  • D. Jenssen
    Jenssen is a Scandinavian surname, particularly common in Norway, that originated as a patronymic form meaning "son of Jens."
  • E. Niva
    Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
  • 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: Jensen
Triple: [The Losers (2010 film), mainCharacter, Jensen]
Generated description
Jensen is the wisecracking, tech-savvy hacker and communications expert on the black-ops team in the action film "The Losers" (2010).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jensen
Target entity description: Jensen is the wisecracking, tech-savvy hacker and communications expert on the black-ops team in the action film "The Losers" (2010).
  • A. Jensen
    Jensen is a Scandinavian-origin surname and given name, most commonly associated with Danish and Norwegian patronymic naming traditions.
  • B. Jensen
    Jensen is a key crew member aboard the Cloverfield space station in the science fiction horror film "The Cloverfield Paradox," whose actions and fate are central to the movie’s interdimensional crisis.
  • C. Jenson
    Jenson is a given name and surname of English origin, commonly used as a variant spelling of Jensen.
  • D. Jenssen
    Jenssen is a Scandinavian surname, particularly common in Norway, that originated as a patronymic form meaning "son of Jens."
  • E. Niva
    Niva was a prominent Russian literary and illustrated weekly magazine of the late 19th and early 20th centuries, known for publishing fiction, poetry, and cultural commentary.
  • 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_69bd43f07fa48190954317d01600994a completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd64c83af48190bd57be79c1505e9d completed March 20, 2026, 3:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69be3a561a7c8190a5ab87751ab36e0d completed March 21, 2026, 6:27 a.m.
NEDg Description generation batch_69be3d2063e48190afb3fdfd5ad6749f completed March 21, 2026, 6:39 a.m.
NED2 Entity disambiguation (via description) batch_69be3d99a288819088e42e04de5c17a4 completed March 21, 2026, 6:41 a.m.
Created at: March 20, 2026, 1:20 p.m.