Triple

T13205502
Position Surface form Disambiguated ID Type / Status
Subject Eddie Floyd E314348 entity
Predicate notableWork P4 FINISHED
Object Big Bird
Big Bird is the towering, yellow, childlike Muppet character from the children's television show "Sesame Street."
 E290270 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: Big Bird | Statement: [Eddie Floyd, notableWork, Big Bird]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Big Bird
Context triple: [Eddie Floyd, notableWork, Big Bird]
  • A. Big Bird
    Big Bird is a towering yellow bird Muppet from the children's television show "Sesame Street," known for his childlike curiosity and friendly, gentle personality.
  • B. Big Bird
    Big Bird is the nickname for Terminal 1 at Tokyo’s Haneda Airport, a major domestic flight hub known for its extensive shopping and dining facilities.
  • C. BigBird
    BigBird is a transformer-based language model architecture designed to efficiently handle very long sequences using sparse attention mechanisms.
  • D. Elmo
    Elmo is a popular red Muppet character from the children's television show "Sesame Street," known for his cheerful personality and distinctive high-pitched voice.
  • E. Elmo
    Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
  • 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: Big Bird
Triple: [Eddie Floyd, notableWork, Big Bird]
Generated description
Big Bird is the towering, yellow, childlike Muppet character from the children's television show "Sesame Street."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Big Bird
Target entity description: Big Bird is the towering, yellow, childlike Muppet character from the children's television show "Sesame Street."
  • A. Big Bird chosen
    Big Bird is a towering yellow bird Muppet from the children's television show "Sesame Street," known for his childlike curiosity and friendly, gentle personality.
  • B. Big Bird
    Big Bird is the nickname for Terminal 1 at Tokyo’s Haneda Airport, a major domestic flight hub known for its extensive shopping and dining facilities.
  • C. BigBird
    BigBird is a transformer-based language model architecture designed to efficiently handle very long sequences using sparse attention mechanisms.
  • D. Elmo
    Elmo is a popular red Muppet character from the children's television show "Sesame Street," known for his cheerful personality and distinctive high-pitched voice.
  • E. Elmo
    Elmo is a deep contextualized word representation model for natural language processing that captures complex characteristics of word use and syntax across different linguistic contexts.
  • F. None of above.

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_69d806aee7308190b70a237ba2a6e3e1 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98c9b0cf08190a1d71cc94139539d completed April 10, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6f60eee288190bdb3ed6110394e48 completed May 3, 2026, 7:15 a.m.
NEDg Description generation batch_69f6f76ade3c8190b46655f104a1ceaa completed May 3, 2026, 7:21 a.m.
NED2 Entity disambiguation (via description) batch_69f6f85149cc8190adf68387475d3286 completed May 3, 2026, 7:25 a.m.
Created at: April 9, 2026, 9:17 p.m.