Triple

T3529475
Position Surface form Disambiguated ID Type / Status
Subject Simon Callow E74622 entity
Predicate hasWritten P2831 FINISHED
Object Love Is Where It Falls
Love Is Where It Falls is a memoir by British actor and director Simon Callow reflecting on his intense, complex relationship with theatrical agent Peggy Ramsay.
 E365776 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: Love Is Where It Falls | Statement: [Simon Callow, hasWritten, Love Is Where It Falls]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Love Is Where It Falls
Context triple: [Simon Callow, hasWritten, Love Is Where It Falls]
  • A. Fall in Love
    "Fall in Love" is a popular Afrobeat love song by Nigerian artist D'banj that became one of his signature hits across Africa.
  • B. Falling in Love with Love
    "Falling in Love with Love" is a popular show tune by composer Richard Rodgers and lyricist Lorenz Hart, introduced in the 1938 musical "The Boys from Syracuse."
  • C. Love Has Fallen on Me
    "Love Has Fallen on Me" is a soulful jazz track performed by vocalist Chaka Khan, showcasing her expressive early style.
  • D. Fell for You
    "Fell for You" is a pop-punk song by Green Day from their 2012 album ¡Uno!.
  • E. Here’s Love
    "Here’s Love" is a 1963 Broadway musical by Meredith Willson, adapted from the classic film "Miracle on 34th Street."
  • 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: Love Is Where It Falls
Triple: [Simon Callow, hasWritten, Love Is Where It Falls]
Generated description
Love Is Where It Falls is a memoir by British actor and director Simon Callow reflecting on his intense, complex relationship with theatrical agent Peggy Ramsay.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Love Is Where It Falls
Target entity description: Love Is Where It Falls is a memoir by British actor and director Simon Callow reflecting on his intense, complex relationship with theatrical agent Peggy Ramsay.
  • A. Fall in Love
    "Fall in Love" is a popular Afrobeat love song by Nigerian artist D'banj that became one of his signature hits across Africa.
  • B. Falling in Love with Love
    "Falling in Love with Love" is a popular show tune by composer Richard Rodgers and lyricist Lorenz Hart, introduced in the 1938 musical "The Boys from Syracuse."
  • C. Love Has Fallen on Me
    "Love Has Fallen on Me" is a soulful jazz track performed by vocalist Chaka Khan, showcasing her expressive early style.
  • D. Fell for You
    "Fell for You" is a pop-punk song by Green Day from their 2012 album ¡Uno!.
  • E. Here’s Love
    "Here’s Love" is a 1963 Broadway musical by Meredith Willson, adapted from the classic film "Miracle on 34th Street."
  • 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_69ad85d1a3948190931fd1ea1f49717b completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc9764a881908aa8d25dc9adf59e completed March 8, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69b37e93c1988190a9ab7698bf63e8e6 completed March 13, 2026, 3:03 a.m.
NEDg Description generation batch_69b380a6b6ec8190be0741cb9535b650 completed March 13, 2026, 3:12 a.m.
NED2 Entity disambiguation (via description) batch_69b3812927e48190a84f3c7fa55d070a completed March 13, 2026, 3:14 a.m.
Created at: March 8, 2026, 3:19 p.m.