Triple

T13321734
Position Surface form Disambiguated ID Type / Status
Subject Mysore Airport E317330 entity
Predicate cityServed P82 FINISHED
Object Mysuru E80753 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: Mysuru | Statement: [Mysore Airport, cityServed, Mysuru]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mysuru
Context triple: [Mysore Airport, cityServed, Mysuru]
  • A. Mysuru chosen
    Mysuru is a historic city in the southern Indian state of Karnataka, renowned for its royal heritage, palaces, and cultural festivals such as Dasara.
  • B. Madikeri
    Madikeri is a scenic hill town in Karnataka’s Coorg region, known for its cool climate, coffee plantations, and lush Western Ghats landscapes.
  • C. Davangere
    Davangere is a major city in central Karnataka, India, known for its textile industry, educational institutions, and distinctive local cuisine such as Davangere benne dosa.
  • D. Sullia
    Sullia is a town in the Dakshina Kannada district of Karnataka, India, known as a local commercial and educational center in the region.
  • E. 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.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d9992ab83c8190982d9f54dff6919f completed April 11, 2026, 12:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7ce06488190b18e48dfba240024 completed May 7, 2026, 8:36 p.m.
Created at: April 9, 2026, 9:30 p.m.