Triple

T13258557
Position Surface form Disambiguated ID Type / Status
Subject Teddy Dunn E315727 entity
Predicate givenName P17 FINISHED
Object Teddy
Teddy is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
 E1031670 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: Teddy | Statement: [Teddy Dunn, givenName, Teddy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Teddy
Context triple: [Teddy Dunn, givenName, Teddy]
  • A. Teddy
    Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
  • B. Teddy
    Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
  • C. Teddy
    Teddy is the young English boy in Rudyard Kipling’s story “Rikki-Tikki-Tavi,” whose life is saved from deadly cobras by the brave mongoose.
  • D. Teddy
    Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
  • E. Teddy
    Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
  • 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: Teddy
Triple: [Teddy Dunn, givenName, Teddy]
Generated description
Teddy is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Teddy
Target entity description: Teddy is a masculine given name, often a diminutive of Theodore or Edward, commonly used in English-speaking countries.
  • A. Teddy
    Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
  • B. Teddy
    Teddy is Mr. Bean’s beloved brown teddy bear, a silent yet expressive companion that often serves as his confidant and playmate in the comedy series.
  • C. Teddy
    Teddy is a character in Louisa May Alcott’s novel "Jo’s Boys," part of the continuation of the March family saga begun in "Little Women."
  • D. Teddy
    Teddy is a recurring character on the animated TV show "Bob's Burgers," known as the Belcher family's loyal but somewhat bumbling handyman and regular customer.
  • E. Teddy
    Teddy is the young English boy in Rudyard Kipling’s story “Rikki-Tikki-Tavi,” whose life is saved from deadly cobras by the brave mongoose.
  • 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_69d806b1d9ac8190852c5571d5bd5f0f completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98f778088819082b8a596c04bfe02 completed April 11, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69f70a444b4c8190a5dd95460ac96cc7 completed May 3, 2026, 8:41 a.m.
NEDg Description generation batch_69f70d50b6148190a0cbc31d9937d59e completed May 3, 2026, 8:54 a.m.
NED2 Entity disambiguation (via description) batch_69f70e14389881908787a64ddead9707 completed May 3, 2026, 8:57 a.m.
Created at: April 9, 2026, 9:25 p.m.