Triple

T12875258
Position Surface form Disambiguated ID Type / Status
Subject Nine Stories E307948 entity
Predicate hasStory P11859 FINISHED
Object Teddy
Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
E1007615 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: [Nine Stories, hasStory, Teddy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teddy
Context triple: [Nine Stories, hasStory, Teddy]
  • A. 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.
  • B. 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."
  • C. Teddy
    Teddy is the nickname of Teddy Kollek, the long-serving and influential former mayor of Jerusalem.
  • D. Teddy
    Teddy is a key supporting character in the film "Memento," serving as a dubious ally whose true motives and identity are gradually revealed through the story's nonlinear narrative.
  • 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
  • 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: [Nine Stories, hasStory, Teddy]
Generated description
Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Teddy
Target entity description: Teddy is a short story by J.D. Salinger that follows a spiritually precocious child whose philosophical insights unsettle the adults around him.
  • 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 young English boy in Rudyard Kipling’s story “Rikki-Tikki-Tavi,” whose life is saved from deadly cobras by the brave mongoose.
  • C. Teddy
    Teddy is a central character in Harold Pinter’s play "The Homecoming," serving as the seemingly detached, intellectual son whose return to his family home triggers the drama’s power struggles and psychological tensions.
  • D. 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.
  • E. Teddy
    Teddy is a key supporting character in the film "Memento," serving as a dubious ally whose true motives and identity are gradually revealed through the story's nonlinear narrative.
  • 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_69d7bdf69bc48190af6c2621f28ca351 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970f97f9c81908c75259a4cab1d3c completed April 10, 2026, 9:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69bb679f88190a1799b73c3f738b6 completed May 3, 2026, 12:49 a.m.
NEDg Description generation batch_69f69fb238f08190a0c63d71bfbe4529 completed May 3, 2026, 1:06 a.m.
NED2 Entity disambiguation (via description) batch_69f6a087120c81908644ed732eff4d99 completed May 3, 2026, 1:10 a.m.
Created at: April 9, 2026, 5:38 p.m.