Triple

T17612680
Position Surface form Disambiguated ID Type / Status
Subject AGC E429000 entity
Predicate connectsToCity P4245 FINISHED
Object Jaipur 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: Jaipur | Statement: [AGC, connectsToCity, Jaipur]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jaipur
Context triple: [AGC, connectsToCity, Jaipur]
  • A. Jaipur chosen
    Jaipur is a major historic city in northwestern India, famed for its pink-hued architecture, royal palaces, and role as a key cultural and tourist center.
  • B. Jodhpur
    Jodhpur is a historic city in the Indian state of Rajasthan, renowned for its blue-painted old town, imposing Mehrangarh Fort, and role as a major cultural and commercial center on the edge of the Thar Desert.
  • C. Udaipur
    Udaipur is a historic town in the Indian state of Tripura, known for its ancient temples and scenic lakes.
  • D. Udaipur
    Udaipur is a historic city in India renowned for its lakes, palaces, and role as a former capital of the Mewar kingdom.
  • E. Udaipur
    Udaipur is a town in the Lahaul and Spiti district of Himachal Pradesh, India, known for its scenic Himalayan setting and the ancient Mrikula Devi Temple.
  • 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_69d889e1c6148190ba76241e74688f8b completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46d2eaa348190a8226eef8c0d6e31 completed April 19, 2026, 5:50 a.m.
Created at: April 10, 2026, 5:51 a.m.