Triple

T3785111
Position Surface form Disambiguated ID Type / Status
Subject The Opposite of Sex E85511 entity
Predicate character P662 FINISHED
Object Matt
Matt is a fictional character from the dark comedy film "The Opposite of Sex," which follows the chaotic fallout of a manipulative teenager’s impact on the lives of those around her.
 E387842 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: Matt | Statement: [The Opposite of Sex, character, Matt]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Matt
Context triple: [The Opposite of Sex, character, Matt]
  • A. Matt
    Matt is the given name of Matt Eberflus, an American football coach best known as the head coach of the Chicago Bears in the NFL.
  • B. Matty
    Matty is a common diminutive or nickname for the given name Matthew.
  • C. Matty
    Matty is the famous nickname of Christy Mathewson, one of early baseball’s greatest pitchers and a Hall of Famer for the New York Giants.
  • D. Mark
    Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
  • E. Mark
    Mark is a river in the southern Netherlands and northern Belgium that flows through the province of North Brabant before joining the Dintel.
  • 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: Matt
Triple: [The Opposite of Sex, character, Matt]
Generated description
Matt is a fictional character from the dark comedy film "The Opposite of Sex," which follows the chaotic fallout of a manipulative teenager’s impact on the lives of those around her.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Matt
Target entity description: Matt is a fictional character from the dark comedy film "The Opposite of Sex," which follows the chaotic fallout of a manipulative teenager’s impact on the lives of those around her.
  • A. Matt
    Matt is the given name of Matt Eberflus, an American football coach best known as the head coach of the Chicago Bears in the NFL.
  • B. Matty
    Matty is a common diminutive or nickname for the given name Matthew.
  • C. Matty
    Matty is the famous nickname of Christy Mathewson, one of early baseball’s greatest pitchers and a Hall of Famer for the New York Giants.
  • D. Mark
    Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
  • E. Mark
    Mark is a river in the southern Netherlands and northern Belgium that flows through the province of North Brabant before joining the Dintel.
  • 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_69aed937fa8881908208ef3801060826 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aee3dd80f08190a1704521a764e22c completed March 9, 2026, 3:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4f04a35448190a57f431ef703b1e1 completed March 14, 2026, 5:21 a.m.
NEDg Description generation batch_69b4f159d7e88190a76d51378ba141d3 completed March 14, 2026, 5:25 a.m.
NED2 Entity disambiguation (via description) batch_69b4f220f9388190b2c3615f713f01f2 completed March 14, 2026, 5:29 a.m.
Created at: March 9, 2026, 3:13 p.m.