Triple

T11792414
Position Surface form Disambiguated ID Type / Status
Subject Pankaj Kapur E280419 entity
Predicate notableWork P4 FINISHED
Object Finding Fanny E528621 NE FINISHED

How this triple was built (2 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: Finding Fanny | Statement: [Pankaj Kapur, notableWork, Finding Fanny]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Finding Fanny
Context triple: [Pankaj Kapur, notableWork, Finding Fanny]
  • A. Finding Fanny chosen
    Finding Fanny is a 2014 Indian satirical road comedy film set in Goa that follows a quirky group of characters on a journey to find a postman's long-lost love.
  • B. Fanny Herself
    Fanny Herself is a 1917 coming-of-age novel by Edna Ferber that follows an ambitious young Jewish woman striving for independence and success in early 20th-century America.
  • C. Fainting Fancies
    Fainting Fancies are a joke-shop sweet from the Harry Potter series that causes the eater to briefly faint, often used by students to escape classes.
  • D. Bye, Felicia
    "Bye, Felicia" is a dismissive catchphrase from 1990s American pop culture, popularized by the film Friday and later widely used to indicate that someone is unimportant or can be easily dismissed.
  • E. Franny
    Franny is a common diminutive or nickname for the given name Frances.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6ab258b808190b1735835c841e3a4 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a588d2c881909783c2d678c2a474 completed April 10, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69f09107fd2481908d765d2188035012 completed April 28, 2026, 10:50 a.m.
Created at: April 8, 2026, 9:42 p.m.