Triple

T2214981
Position Surface form Disambiguated ID Type / Status
Subject Slumdog Millionaire E48010 entity
Predicate basedOn P98 FINISHED
Object Q & A
Q & A is a novel by Vikas Swarup that follows a poor Indian waiter who unexpectedly wins a fortune on a quiz show, inspiring the film Slumdog Millionaire.
 E243947 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: Q & A | Statement: [Slumdog Millionaire, basedOn, Q & A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Q & A
Context triple: [Slumdog Millionaire, basedOn, Q & A]
  • A. Q’s
    Q’s is the nickname commonly used for the former American Basketball Association team the San Diego Conquistadors.
  • B. Some Answered Questions
    Some Answered Questions is a central Bahá'í text consisting of recorded talks by ʻAbdu'l‑Bahá that explain key spiritual, philosophical, and scriptural themes of the Bahá'í Faith.
  • C. Q
    The Q is a New York City Subway service that runs along the BMT Broadway Line in Manhattan and the Brighton Line in Brooklyn, providing crosstown and interborough transit.
  • D. Q
    Q is a recurring comedic character from the James Bond film series, known as the eccentric head of MI6's gadget and technology division.
  • E. QA
    QA is the two-letter ISO 3166-1 alpha-2 country code assigned to Qatar for international standardization and identification.
  • 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: Q & A
Triple: [Slumdog Millionaire, basedOn, Q & A]
Generated description
Q & A is a novel by Vikas Swarup that follows a poor Indian waiter who unexpectedly wins a fortune on a quiz show, inspiring the film Slumdog Millionaire.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Q & A
Target entity description: Q & A is a novel by Vikas Swarup that follows a poor Indian waiter who unexpectedly wins a fortune on a quiz show, inspiring the film Slumdog Millionaire.
  • A. Q’s
    Q’s is the nickname commonly used for the former American Basketball Association team the San Diego Conquistadors.
  • B. Some Answered Questions
    Some Answered Questions is a central Bahá'í text consisting of recorded talks by ʻAbdu'l‑Bahá that explain key spiritual, philosophical, and scriptural themes of the Bahá'í Faith.
  • C. Q
    The Q is a New York City Subway service that runs along the BMT Broadway Line in Manhattan and the Brighton Line in Brooklyn, providing crosstown and interborough transit.
  • D. Q
    Q is a recurring comedic character from the James Bond film series, known as the eccentric head of MI6's gadget and technology division.
  • E. QA
    QA is the two-letter ISO 3166-1 alpha-2 country code assigned to Qatar for international standardization and identification.
  • 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_69a88aa1ee708190862c8c378c41e9eb completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abbff11574819091d1b50d637ae767 completed March 7, 2026, 6:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6554f3308190a180cac3ad7e2ce4 completed March 9, 2026, 6:14 a.m.
NEDg Description generation batch_69ae65d419048190ad723d21ab7f1cab completed March 9, 2026, 6:16 a.m.
NED2 Entity disambiguation (via description) batch_69ae666e71908190b50be2cac5bdfa28 completed March 9, 2026, 6:19 a.m.
Created at: March 4, 2026, 7:46 p.m.