Triple

T15038938
Position Surface form Disambiguated ID Type / Status
Subject Blue’s Clues E378549 entity
Predicate mainCharacter P1183 FINISHED
Object 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.
E1136081 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: [Blue’s Clues, mainCharacter, Steve]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steve
Context triple: [Blue’s Clues, mainCharacter, Steve]
  • A. Steve
    Steve is the familiar given name of Stephen Gary Wozniak, the pioneering American computer engineer and co-founder of Apple Inc.
  • B. Steve
    Steve is the central protagonist of the romantic comedy film "Him & Her," around whom the story’s relationships and conflicts revolve.
  • C. 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.
  • 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: [Blue’s Clues, mainCharacter, Steve]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Steve
Target entity description: 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.
  • A. Steve
    Steve is the familiar given name of Stephen Gary Wozniak, the pioneering American computer engineer and co-founder of Apple Inc.
  • B. Steve
    Steve is an American daytime talk show hosted by comedian and television personality Steve Harvey, featuring celebrity interviews, human-interest stories, and lifestyle segments.
  • C. Steve
    Steve is the commonly used nickname for Stephen Case, an American entrepreneur best known as the co-founder and former CEO of AOL.
  • D. Steve
    Steve is the central protagonist of the adventure story "High Seas," around whom the main events and conflicts of the narrative revolve.
  • E. 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.
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded82cf3848190b0b2b6c9e65bc70b completed April 15, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69fea5b75c04819085996a7f88ab6c38 completed May 9, 2026, 3:10 a.m.
NEDg Description generation batch_69fea9f91f3c8190bff7dc7a028a382b completed May 9, 2026, 3:28 a.m.
NED2 Entity disambiguation (via description) batch_69feaa469df88190b7f08de78613121d completed May 9, 2026, 3:30 a.m.
Created at: April 10, 2026, 2:59 a.m.