Triple

T25491376
Position Surface form Disambiguated ID Type / Status
Subject Nuvve Kavali E638843 entity
Predicate castMember P1668 FINISHED
Object Vijayalakshmi
Vijayalakshmi is an Indian actress best known for her work in Telugu cinema, particularly in early-2000s romantic films.
E1700788 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: Vijayalakshmi | Statement: [Nuvve Kavali, castMember, Vijayalakshmi]
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: Vijayalakshmi
Triple: [Nuvve Kavali, castMember, Vijayalakshmi]
Generated description
Vijayalakshmi is an Indian actress best known for her work in Telugu cinema, particularly in early-2000s romantic films.

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_69e75dbbd2a88190b70e1e645de14b9a completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f7a6493481908fccf217f6296b95 completed May 2, 2026, 1:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ec8c4e7081909c3f50e2732fe35e completed May 22, 2026, 11:53 p.m.
NEDg Description generation batch_6a10edf7ff0c8190935a637ff0df364b completed May 22, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a10ef6b80248190be0346728653b12a completed May 23, 2026, 12:06 a.m.
Created at: April 21, 2026, 2:38 p.m.