Triple

T189366
Position Surface form Disambiguated ID Type / Status
Subject Sanskrit E3683 entity
Predicate influenced P9 FINISHED
Object Bengali E5055 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: Bengali | Statement: [Sanskrit, influenced, Bengali]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bengali
Context triple: [Sanskrit, influenced, Bengali]
  • A. Bengali chosen
    Bengali is an Indo-Aryan language spoken primarily in the Bengal region of South Asia and serving as the official and most widely used language of Bangladesh and the Indian state of West Bengal.
  • B. Hindi
    Hindi is an Indo-Aryan language widely spoken across northern and central India and used in government, education, media, and popular culture.
  • C. Maithili
    Maithili is an Indo-Aryan language spoken primarily in the eastern Indian state of Bihar and neighboring regions, with a rich literary tradition and official recognition in India.
  • D. Sindhi
    Sindhi is an Indo-Aryan language spoken primarily in Pakistan and India, known for its rich literary tradition and distinct script variants.
  • E. Kannada
    Kannada is a major Dravidian language predominantly spoken in the Indian state of Karnataka and surrounding regions, with a rich literary tradition spanning over a millennium.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a2548debd48190ae3a06d6e65b53c6 completed Feb. 28, 2026, 2:35 a.m.
NER Named-entity recognition batch_69a2594c385481909e1e088e45c460a4 completed Feb. 28, 2026, 2:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69a305e511a08190a560125ed3839a0c completed Feb. 28, 2026, 3:12 p.m.
Created at: Feb. 28, 2026, 2:41 a.m.