Triple

T8444970
Position Surface form Disambiguated ID Type / Status
Subject Santalī E199649 entity
Predicate hasOfficialStatusIn P974 FINISHED
Object Assam E4843 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: Assam | Statement: [Santalī, hasOfficialStatusIn, Assam]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Assam
Context triple: [Santalī, hasOfficialStatusIn, Assam]
  • A. Assam chosen
    Assam is a northeastern region of the Indian subcontinent known for its tea plantations, rich biodiversity, and distinct cultural heritage.
  • B. Tripura
    Tripura is a small, hilly state in northeastern India known for its diverse tribal cultures, historical palaces, and dense forests.
  • C. Arunachal Pradesh
    Arunachal Pradesh is a northeastern Indian state known for its mountainous terrain, diverse indigenous cultures, and strategic location along the borders with China, Bhutan, and Myanmar.
  • D. West Bengal
    West Bengal is an eastern Indian state known for its cultural heritage, literature, and the metropolis of Kolkata (formerly Calcutta).
  • E. Meghalaya
    Meghalaya is a hilly state in northeastern India known for its heavy rainfall, lush forests, and diverse indigenous cultures.
  • 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_69ca83170f9081909cd98f55614c6476 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe3138ee08190918cd82adbe2d9a1 completed March 31, 2026, 3:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6e8ea2748190b9ffa2f36c0e397c completed April 3, 2026, 7:38 a.m.
Created at: March 30, 2026, 6:09 p.m.