Triple

T36928749
Position Surface form Disambiguated ID Type / Status
Subject Khoobsurat (2014 film) E913420 entity
Predicate character P662 FINISHED
Object Vikram Singh Rathore
Vikram Singh Rathore is the disciplined yet charming royal prince and primary love interest portrayed by Fawad Khan in the 2014 Bollywood romantic comedy film "Khoobsurat."
E2222990 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: Vikram Singh Rathore | Statement: [Khoobsurat (2014 film), character, Vikram Singh Rathore]
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: Vikram Singh Rathore
Triple: [Khoobsurat (2014 film), character, Vikram Singh Rathore]
Generated description
Vikram Singh Rathore is the disciplined yet charming royal prince and primary love interest portrayed by Fawad Khan in the 2014 Bollywood romantic comedy film "Khoobsurat."

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_69f76e896c988190880c130e01303dd4 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69f9fde3b0f48190aad9b0386384ea79 completed May 5, 2026, 2:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a406370e36c819093e5d6d2b0a1a6c2 completed June 27, 2026, 11:57 p.m.
NEDg Description generation batch_6a4067d3efc0819085f3ecfaa3df32e2 completed June 28, 2026, 12:16 a.m.
NED2 Entity disambiguation (via description) batch_6a406845d00c8190be33c270073fcde3 completed June 28, 2026, 12:18 a.m.
Created at: May 3, 2026, 4:13 p.m.