Triple

T23148119
Position Surface form Disambiguated ID Type / Status
Subject Gujarati cinema E578248 entity
Predicate distributionRegion P285 FINISHED
Object Rajasthan NE NERFINISHED

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: Rajasthan | Statement: [Gujarati cinema, distributionRegion, Rajasthan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rajasthan
Context triple: [Gujarati cinema, distributionRegion, Rajasthan]
  • A. Rajasthan chosen
    Rajasthan is a northwestern Indian state known for its vast Thar Desert, historic Rajput forts and palaces, and rich cultural heritage.
  • B. Gujarat
    Gujarat is a western coastal state of India known for its significant role in trade and industry, rich cultural heritage, and historic cities such as Ahmedabad.
  • C. Jaipur State
    Jaipur State was a prominent princely state in pre-independence India, ruled by Rajput kings from the Kachwaha dynasty with its capital at the historic city of Jaipur.
  • D. Rajasthan (Malwa area)
    Rajasthan (Malwa area) is a historic region in central India known for its distinctive Malwa painting tradition and rich Rajput-era cultural heritage.
  • E. Chhattisgarh
    Chhattisgarh is a state in central India known for its rich mineral resources, dense forests, tribal cultures, and growing industrial and power sectors.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245fb8de081908f0eba7b5fd75bc4 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18ecf9e9881908991ede784158f1e completed April 29, 2026, 4:53 a.m.
Created at: April 17, 2026, 4:01 p.m.