Triple

T14300744
Position Surface form Disambiguated ID Type / Status
Subject Verity Lambert E354554 entity
Predicate notableWork P4 FINISHED
Object May to December
May to December is a British television sitcom, produced by Verity Lambert, that follows the romantic relationship between a middle-aged solicitor and a much younger woman.
 E1092081 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: May to December | Statement: [Verity Lambert, notableWork, May to December]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: May to December
Context triple: [Verity Lambert, notableWork, May to December]
  • A. From May to October
    "From May to October" is a novel by British writer and painter Jennifer Lash, reflecting her character-driven, literary style.
  • B. Juni
    Juni is a bilingual producer known for creating content across multiple languages.
  • C. Until September
    "Until September" is a 1984 romantic drama film starring Karen Allen as an American tourist who begins an affair with a married French banker while stranded in Paris.
  • D. Late July
    Late July is a snack food brand best known for its organic and non-GMO tortilla chips and crackers.
  • E. June
    June is an early-summer month in the Northern Hemisphere often associated with favorable weather for outdoor activities and mountaineering.
  • 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: May to December
Triple: [Verity Lambert, notableWork, May to December]
Generated description
May to December is a British television sitcom, produced by Verity Lambert, that follows the romantic relationship between a middle-aged solicitor and a much younger woman.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: May to December
Target entity description: May to December is a British television sitcom, produced by Verity Lambert, that follows the romantic relationship between a middle-aged solicitor and a much younger woman.
  • A. From May to October
    "From May to October" is a novel by British writer and painter Jennifer Lash, reflecting her character-driven, literary style.
  • B. Juni
    Juni is a bilingual producer known for creating content across multiple languages.
  • C. Until September
    "Until September" is a 1984 romantic drama film starring Karen Allen as an American tourist who begins an affair with a married French banker while stranded in Paris.
  • D. Late July
    Late July is a snack food brand best known for its organic and non-GMO tortilla chips and crackers.
  • E. June
    June is an early-summer month in the Northern Hemisphere often associated with favorable weather for outdoor activities and mountaineering.
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de717e246c819083e67ac2b3b77881 completed April 14, 2026, 4:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d2697648190beade47df424a9e5 completed May 8, 2026, 1:32 a.m.
NEDg Description generation batch_69fd41335b308190b1d49b214d5206a1 completed May 8, 2026, 1:49 a.m.
NED2 Entity disambiguation (via description) batch_69fd41fcd8b08190802e54e1c18b58e2 completed May 8, 2026, 1:53 a.m.
Created at: April 10, 2026, 1:11 a.m.