Triple

T27307705
Position Surface form Disambiguated ID Type / Status
Subject Devika Rani E689111 entity
Predicate alsoKnownAs P39 FINISHED
Object Devika Rani Roerich
Devika Rani Roerich was a pioneering Indian film actress and producer, often called the "First Lady of Indian cinema" for her influential work in the early Hindi film industry.
 E1768084 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: Devika Rani Roerich | Statement: [Devika Rani, alsoKnownAs, Devika Rani Roerich]
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: Devika Rani Roerich
Triple: [Devika Rani, alsoKnownAs, Devika Rani Roerich]
Generated description
Devika Rani Roerich was a pioneering Indian film actress and producer, often called the "First Lady of Indian cinema" for her influential work in the early Hindi film industry.

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_69ef355b931c8190a63cafaf7bcc008b completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f627b0ae548190bbdacafe61147088 completed May 2, 2026, 4:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a129cb13f58819093a6745e6d13ea60 completed May 24, 2026, 6:37 a.m.
NEDg Description generation batch_6a129e56c1588190b62c831e4b5eb0b8 completed May 24, 2026, 6:44 a.m.
NED2 Entity disambiguation (via description) batch_6a129f8b990881909f3583d524cbe8bb completed May 24, 2026, 6:49 a.m.
Created at: April 27, 2026, 11:25 a.m.