Triple

T22253259
Position Surface form Disambiguated ID Type / Status
Subject Beit Awwa E550030 entity
Predicate hasNearbyLocality P3883 FINISHED
Object Sikka 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: Sikka | Statement: [Beit Awwa, hasNearbyLocality, Sikka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sikka
Context triple: [Beit Awwa, hasNearbyLocality, Sikka]
  • A. Sikka chosen
    Sikka is an Austronesian language spoken primarily by the Sikka people on the island of Flores in eastern Indonesia.
  • B. Kahaku
    Kahaku is Japan’s National Museum of Nature and Science in Tokyo, renowned for its extensive natural history and scientific collections and exhibitions.
  • C. Sikiajhora
    Sikiajhora is a forest stream and wetland area within West Bengal’s Buxa Tiger Reserve, known for its rich biodiversity and boat-based wildlife viewing.
  • D. Seppa
    Seppa is a town in the East Kameng district of Arunachal Pradesh in northeastern India, serving as an administrative and cultural center in the Himalayan foothills.
  • E. Matsukata
    Matsukata is a Japanese surname most notably associated with Matsukata Masayoshi, a prominent Meiji-era statesman and former Prime Minister of Japan.
  • 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_69e11e42adb8819087714772ea606709 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f138c0c4f48190a75473a7835014f1 completed April 28, 2026, 10:46 p.m.
Created at: April 16, 2026, 8:39 p.m.