Triple

T15289408
Position Surface form Disambiguated ID Type / Status
Subject Mr. Right (2015 film) E365487 entity
Predicate character P662 FINISHED
Object Steve
Steve is a supporting character in the 2015 romantic comedy film "Mr. Right."
E1147847 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: Steve | Statement: [Mr. Right (2015 film), character, Steve]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steve
Context triple: [Mr. Right (2015 film), character, Steve]
  • A. Steve
    Steve is the original human host of the children’s television series "Blue’s Clues," known for his green striped shirt and interactive problem-solving with viewers.
  • B. Steve
    Steve is a central white homeowner character in the play "Clybourne Park," often embodying the tensions and awkward defenses of privilege in the story’s exploration of race and gentrification.
  • C. Steve
    Steve is the familiar given name of Stephen Gary Wozniak, the pioneering American computer engineer and co-founder of Apple Inc.
  • D. Steve
    Steve is the commonly used nickname for Stephen Case, an American entrepreneur best known as the co-founder and former CEO of AOL.
  • E. Steve
    Steve is a masculine given name commonly used in English-speaking countries, often as a short form of Stephen or Steven.
  • 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: Steve
Triple: [Mr. Right (2015 film), character, Steve]
Generated description
Steve is a supporting character in the 2015 romantic comedy film "Mr. Right."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Steve
Target entity description: Steve is a supporting character in the 2015 romantic comedy film "Mr. Right."
  • A. Steve
    Steve is the central protagonist of the romantic comedy film "Him & Her," around whom the story’s relationships and conflicts revolve.
  • B. Steve
    Steve is a supporting character in Woody Allen’s 2016 romantic drama film "Café Society," set against the backdrop of 1930s Hollywood and New York high society.
  • C. Steve
    Steve is a character best known as the calculating antagonist and betrayer in the heist film "The Italian Job."
  • D. Steve
    Steve is a central white homeowner character in the play "Clybourne Park," often embodying the tensions and awkward defenses of privilege in the story’s exploration of race and gentrification.
  • E. Steve
    Steve is the central protagonist of the adventure story "High Seas," around whom the main events and conflicts of the narrative revolve.
  • 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_69d85a103d9081908c1ea6c4c73ac8e3 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e00e5635b4819092a69b5806d15bff completed April 15, 2026, 10:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69feef7d4da4819080f101c3a525ea11 completed May 9, 2026, 8:25 a.m.
NEDg Description generation batch_69fef050d1588190942e1d3f3083607a completed May 9, 2026, 8:29 a.m.
NED2 Entity disambiguation (via description) batch_69fef19d55588190b816d4c37f3a7010 completed May 9, 2026, 8:34 a.m.
Created at: April 10, 2026, 3:15 a.m.