Triple

T5557321
Position Surface form Disambiguated ID Type / Status
Subject Service with a Smile E145676 entity
Predicate featuresCharacter P626 FINISHED
Object Myrtle
Myrtle is a fictional character appearing in P. G. Wodehouse’s comic novel "Service with a Smile."
 E532007 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: Myrtle | Statement: [Service with a Smile, featuresCharacter, Myrtle]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Myrtle
Context triple: [Service with a Smile, featuresCharacter, Myrtle]
  • A. Melaleuca
    Melaleuca is a genus of flowering plants in the myrtle family, best known for species like the tea tree that produce aromatic oils used in medicine and cosmetics.
  • B. Palmetto
    Palmetto is a long-distance Amtrak passenger train service operating along the U.S. East Coast between New York City and Savannah, Georgia.
  • C. DeBary
    DeBary is a small city in central Florida known as a residential community along the St. Johns River in Volusia County.
  • D. Myrties
    Myrties is a coastal village on the Greek island of Kalymnos, known for its beach, views of the islet Telendos, and role as a base for climbers and tourists.
  • E. Lantana
    Lantana is a 2001 Australian psychological drama film directed by Ray Lawrence, acclaimed for its intricate, interwoven narrative about relationships, trust, and betrayal.
  • 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: Myrtle
Triple: [Service with a Smile, featuresCharacter, Myrtle]
Generated description
Myrtle is a fictional character appearing in P. G. Wodehouse’s comic novel "Service with a Smile."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Myrtle
Target entity description: Myrtle is a fictional character appearing in P. G. Wodehouse’s comic novel "Service with a Smile."
  • A. Melaleuca
    Melaleuca is a genus of flowering plants in the myrtle family, best known for species like the tea tree that produce aromatic oils used in medicine and cosmetics.
  • B. Palmetto
    Palmetto is a long-distance Amtrak passenger train service operating along the U.S. East Coast between New York City and Savannah, Georgia.
  • C. DeBary
    DeBary is a small city in central Florida known as a residential community along the St. Johns River in Volusia County.
  • D. Myrties
    Myrties is a coastal village on the Greek island of Kalymnos, known for its beach, views of the islet Telendos, and role as a base for climbers and tourists.
  • E. Lantana
    Lantana is a 2001 Australian psychological drama film directed by Ray Lawrence, acclaimed for its intricate, interwoven narrative about relationships, trust, and betrayal.
  • 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_69c008fcaf788190bafa02a1917ee73b completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01ffe12cc81908186f28ace0f4d82 completed March 22, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0283ebcd081909c86ced90c44266f completed March 22, 2026, 5:34 p.m.
NEDg Description generation batch_69c04441145481909fb7bd26dab129bd completed March 22, 2026, 7:34 p.m.
NED2 Entity disambiguation (via description) batch_69c044a919ec81909469793af9f837ea completed March 22, 2026, 7:36 p.m.
Created at: March 22, 2026, 3:36 p.m.