Triple

T13610949
Position Surface form Disambiguated ID Type / Status
Subject Coorg E325184 entity
Predicate contains P35 FINISHED
Object Madikeri E324694 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: Madikeri | Statement: [Coorg, contains, Madikeri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Madikeri
Context triple: [Coorg, contains, Madikeri]
  • A. Madikeri chosen
    Madikeri is a scenic hill town in Karnataka’s Coorg region, known for its cool climate, coffee plantations, and lush Western Ghats landscapes.
  • B. Sullia
    Sullia is a town in the Dakshina Kannada district of Karnataka, India, known as a local commercial and educational center in the region.
  • C. Mysuru
    Mysuru is a historic city in the southern Indian state of Karnataka, renowned for its royal heritage, palaces, and cultural festivals such as Dasara.
  • D. Mangalore
    Mangalore is a major port city on the southwestern coast of India, known for its maritime trade, diverse culture, and role as a commercial hub of Karnataka.
  • E. Gadag
    Gadag is a historic town in Karnataka, India, renowned for its richly carved medieval temples and monuments from the Western Chalukya architectural tradition.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0aa9a1481908c6f92495aff86c6 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69feb7ca3e348190a75c1dd8aec73a40 completed May 9, 2026, 4:27 a.m.
Created at: April 9, 2026, 9:50 p.m.