Triple

T13020676
Position Surface form Disambiguated ID Type / Status
Subject Virajpet E326161 entity
Predicate district P2709 FINISHED
Object Kodagu E325183 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: Kodagu | Statement: [Virajpet, district, Kodagu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kodagu
Context triple: [Virajpet, district, Kodagu]
  • A. Kaniguram
    Kaniguram is a historic town in South Waziristan, Pakistan, known as a traditional center of the Ormur (Burki) ethnic community.
  • B. Kayadhu
    Kayadhu is a figure in Hindu mythology known as the wife of the demon king Hiranyakashipu and the mother of the devotee Prahlada.
  • C. Kolathunadu
    Kolathunadu was a prominent medieval kingdom and historical region in northern Kerala, India, ruled by the Kolathiri dynasty and centered around present-day Kannur.
  • D. Kodagu district chosen
    Kodagu district is a hilly, coffee-growing region in the Western Ghats of Karnataka, India, known for its scenic landscapes, rich biodiversity, and distinct Kodava culture.
  • E. Kodaikanal
    Kodaikanal is a popular hill station in southern India known for its cool climate, scenic lakes, and lush, forested landscapes.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ecf21bc819082fb512bc479b4be completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6d5f861188190892b4d693395cc5e completed May 3, 2026, 4:58 a.m.
Created at: April 9, 2026, 8:51 p.m.