Triple

T20784726
Position Surface form Disambiguated ID Type / Status
Subject The Green Years E511597 entity
Predicate character P662 FINISHED
Object Susan
Susan is a fictional character from the coming-of-age drama novel and film "The Green Years."
E1450300 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: Susan | Statement: [The Green Years, character, Susan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Susan
Context triple: [The Green Years, character, Susan]
  • A. Susan
    Susan is a friendly human character on Sesame Street who often interacts warmly with Big Bird and the other residents of the neighborhood.
  • B. Susan
    Susan is the birth name of American actress Sigourney Weaver, renowned for her iconic roles in science fiction and horror films such as the Alien franchise.
  • C. Susan
    Susan is a supporting character in the "Nosedive" episode of the anthology television series Black Mirror.
  • D. Susan
    Susan is a feminine given name of Hebrew origin meaning "lily" that has been widely used in English-speaking countries.
  • E. Susan
    Susan is the given name of American painter and photographer Susan Macdowell Eakins, known for her portraits and still lifes.
  • 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: Susan
Triple: [The Green Years, character, Susan]
Generated description
Susan is a fictional character from the coming-of-age drama novel and film "The Green Years."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Susan
Target entity description: Susan is a fictional character from the coming-of-age drama novel and film "The Green Years."
  • A. Susan
    Susan is a central fictional character in Julian Barnes's novel "The Only Story," known for her complex, unconventional romantic relationship with the protagonist.
  • B. Susan
    Susan is one of the central figures in Virginia Woolf’s novel "The Waves," characterized by her deep connection to nature, domestic life, and intense emotional interiority.
  • C. Susan
    Susan is one of the central child protagonists in Alan Garner’s fantasy novel "The Weirdstone of Brisingamen," who becomes embroiled in a magical struggle in rural Cheshire.
  • D. Susan
    "Susan" is a character from the 1985 film "Desperately Seeking Susan," in which Rosanna Arquette starred alongside Madonna.
  • E. Susan
    Susan is a central character in the musical "Tick, Tick... Boom!", serving as Jonathan Larson's girlfriend whose personal and professional aspirations create tension in their relationship.
  • 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_69e0b4cac7a48190a715cb3d545df2b4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c28b4ce88190a45f1c99b58d18eb completed April 21, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08ef9bcc6c8190a594a7c083cca572 completed May 16, 2026, 10:28 p.m.
NEDg Description generation batch_6a08f36a492481908518eba744452df0 completed May 16, 2026, 10:44 p.m.
NED2 Entity disambiguation (via description) batch_6a08f43e36588190a556a19b9dbb738b completed May 16, 2026, 10:48 p.m.
Created at: April 16, 2026, 12:38 p.m.