Triple

T8559095
Position Surface form Disambiguated ID Type / Status
Subject Vikram (2022 film) E202645 entity
Predicate featuresCharacter P626 FINISHED
Object Agent Vikram
Agent Vikram is a fictional intelligence officer and the central protagonist of the 2022 Indian action thriller film "Vikram."
E743922 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: Agent Vikram | Statement: [Vikram (2022 film), featuresCharacter, Agent Vikram]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agent Vikram
Context triple: [Vikram (2022 film), featuresCharacter, Agent Vikram]
  • A. Veer
    Veer is an honorific title in India signifying bravery and valor, often bestowed on distinguished warriors or freedom fighters such as Kunwar Singh.
  • B. Agent 355
    Agent 355 is a skilled and enigmatic covert operative from the Culper Ring who serves as Yorick Brown’s protector in the post-apocalyptic comic series "Y: The Last Man."
  • C. Agent 326
    Agent 326 is a fictional spy character known for undertaking covert missions and espionage activities.
  • D. Ashvajit
    Ashvajit is a Buddhist figure traditionally regarded as one of the early disciples present at the Buddha’s first sermon, the Turning of the Wheel of Dharma.
  • E. Mehta
    Mehta is a common Indian surname associated with various communities, often linked to professions such as merchants, accountants, and administrators.
  • 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: Agent Vikram
Triple: [Vikram (2022 film), featuresCharacter, Agent Vikram]
Generated description
Agent Vikram is a fictional intelligence officer and the central protagonist of the 2022 Indian action thriller film "Vikram."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Agent Vikram
Target entity description: Agent Vikram is a fictional intelligence officer and the central protagonist of the 2022 Indian action thriller film "Vikram."
  • A. Veer
    Veer is an honorific title in India signifying bravery and valor, often bestowed on distinguished warriors or freedom fighters such as Kunwar Singh.
  • B. Agent 355
    Agent 355 is a skilled and enigmatic covert operative from the Culper Ring who serves as Yorick Brown’s protector in the post-apocalyptic comic series "Y: The Last Man."
  • C. Agent 326
    Agent 326 is a fictional spy character known for undertaking covert missions and espionage activities.
  • D. Ashvajit
    Ashvajit is a Buddhist figure traditionally regarded as one of the early disciples present at the Buddha’s first sermon, the Turning of the Wheel of Dharma.
  • E. Mehta
    Mehta is a common Indian surname associated with various communities, often linked to professions such as merchants, accountants, and administrators.
  • 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_69ca8326e6c881908ff720d6abaebdc5 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe9485dd88190bc2cf2adf39d48ee completed March 31, 2026, 3:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce894d82588190b558d5b2dc65eafe completed April 2, 2026, 3:20 p.m.
NEDg Description generation batch_69ce8a9ce1a08190a579f7f7a0319d01 completed April 2, 2026, 3:26 p.m.
NED2 Entity disambiguation (via description) batch_69ce8bdf1f148190ac832424661bd8e5 completed April 2, 2026, 3:31 p.m.
Created at: March 30, 2026, 6:20 p.m.