Triple

T21992546
Position Surface form Disambiguated ID Type / Status
Subject Uttar Pradesh E509513 entity
Predicate hasAirport P105 FINISHED
Object Agra Airport 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: Agra Airport | Statement: [Uttar Pradesh, hasAirport, Agra Airport]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agra Airport
Context triple: [Uttar Pradesh, hasAirport, Agra Airport]
  • A. Agra Airport chosen
    Agra Airport is a domestic airport in the city of Agra, India, serving as a key air gateway to the Taj Mahal and other nearby tourist attractions.
  • B. Ayodhya Airport
    Ayodhya Airport is a modern international airport serving the city of Ayodhya in Uttar Pradesh, India, developed to handle growing religious tourism and regional air traffic.
  • C. Prayagraj Airport
    Prayagraj Airport is a domestic airport serving the city of Prayagraj (formerly Allahabad) in the Indian state of Uttar Pradesh.
  • D. Kanpur Airport
    Kanpur Airport is a domestic airport serving the city of Kanpur in Uttar Pradesh, India, handling regional passenger flights and limited commercial operations.
  • E. Varanasi Airport
    Varanasi Airport, now known as Lal Bahadur Shastri International Airport, is the main air gateway serving the ancient city of Varanasi in Uttar Pradesh, India.
  • 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_69e11e2c814c8190837d072789000486 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1270e951081908deda039b47ca84b completed April 28, 2026, 9:30 p.m.
Created at: April 16, 2026, 8:15 p.m.