Triple

T4908871
Position Surface form Disambiguated ID Type / Status
Subject Gurdaspur district E110180 entity
Predicate hasHistoricalRegion P915 FINISHED
Object Majha E63912 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: Majha | Statement: [Gurdaspur district, hasHistoricalRegion, Majha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Majha
Context triple: [Gurdaspur district, hasHistoricalRegion, Majha]
  • A. Majha chosen
    Majha is a culturally significant region of Punjab in northern India, traditionally known as the heartland of Sikh culture and history.
  • B. Mahjoor
    Mahjoor was a renowned Kashmiri poet celebrated for revitalizing modern Kashmiri literature and expressing the social and cultural aspirations of the Kashmiri people.
  • C. Marga Marga
    Marga Marga is a river in central Chile whose name was adopted by the surrounding Province of Marga Marga.
  • D. Mihna
    The Mihna was an Islamic inquisition instituted in the 9th century that tested and persecuted scholars over their adherence to the doctrine of the createdness of the Qur’an.
  • E. Marda Kunama
    Marda Kunama is a regional dialect of the Kunama language spoken by the Kunama people of the Horn of Africa.
  • 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_69bd44132b94819088522d92beaadc78 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e765094819099481f4f2dd7c47d completed March 20, 2026, 3:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fe098b081908e17d6d349b76364 completed March 21, 2026, 10:16 a.m.
Created at: March 20, 2026, 1:29 p.m.