Triple

T13901544
Position Surface form Disambiguated ID Type / Status
Subject Khadur Sahib E334233 entity
Predicate region P40 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: [Khadur Sahib, region, Majha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Majha
Context triple: [Khadur Sahib, region, 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. Mam Maya
    Mam Maya are an Indigenous Maya people of the highlands of Guatemala and parts of Mexico, known for their distinct Mayan language, traditional weaving, and resilient cultural practices.
  • C. Majhli Didi
    Majhli Didi is a 1967 Hindi drama film, based on a story by Sarat Chandra Chattopadhyay and noted for its sensitive portrayal of rural life and relationships.
  • D. Mahjoor
    Mahjoor was a renowned Kashmiri poet celebrated for revitalizing modern Kashmiri literature and expressing the social and cultural aspirations of the Kashmiri people.
  • E. Marga Marga
    Marga Marga is a river in central Chile whose name was adopted by the surrounding Province of Marga Marga.
  • 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_69d81c5eaa9c819083b1ff8689179565 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de25d9c7a48190ad8fb0ca676f4f7b completed April 14, 2026, 11:32 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7c722e72081909090b2d64000ebd9 completed May 3, 2026, 10:07 p.m.
Created at: April 9, 2026, 10:15 p.m.