Triple

T3731811
Position Surface form Disambiguated ID Type / Status
Subject Tibetan script E79081 entity
Predicate primaryRegion P1103 FINISHED
Object Sikkim E177807 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: Sikkim | Statement: [Tibetan script, primaryRegion, Sikkim]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sikkim
Context triple: [Tibetan script, primaryRegion, Sikkim]
  • A. Sikkim chosen
    Sikkim is a small, mountainous Indian state in the eastern Himalayas known for its dramatic landscapes, Buddhist monasteries, and proximity to Mount Kanchenjunga.
  • B. Meghalaya
    Meghalaya is a hilly state in northeastern India known for its heavy rainfall, lush forests, and diverse indigenous cultures.
  • C. Himachal Pradesh
    Himachal Pradesh is a mountainous state in northern India known for its Himalayan landscapes, hill stations, and tourism.
  • D. Uttarakhand
    Uttarakhand is a northern Indian state in the Himalayas known for its sacred rivers, pilgrimage sites, and mountainous landscapes.
  • E. 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.
  • 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_69ad8b0e4650819090ad7cef094285e8 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb21002c81908438170ed6f6c271 completed March 8, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b7446bf88190848eb7ecb0e067bd completed March 14, 2026, 7:30 p.m.
Created at: March 8, 2026, 3:34 p.m.