Triple

T22543532
Position Surface form Disambiguated ID Type / Status
Subject Gaya Junction railway station E557356 entity
Predicate servesCity P82 FINISHED
Object Gaya 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: Gaya | Statement: [Gaya Junction railway station, servesCity, Gaya]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gaya
Context triple: [Gaya Junction railway station, servesCity, Gaya]
  • A. Gaya chosen
    Gaya is a historic city in the Indian state of Bihar, renowned as a major Hindu and Buddhist pilgrimage center, especially for the Vishnupad Temple and its proximity to Bodh Gaya.
  • B. Gaya
    Gaya is a historic town and important urban center in northern Nigeria’s Kano State.
  • C. Gaya
    Gaya was an ancient Korean confederacy of city-states known for its advanced iron culture and maritime trade, located in the southern part of the Korean Peninsula.
  • D. Gairo
    Gairo is a town and district in central Tanzania known as an agricultural hub and a key stop along the main highway between Dar es Salaam and the country’s interior.
  • E. Gayo
    Gayo is an Austronesian language spoken by the Gayo people in the central highlands of northern Sumatra, Indonesia.
  • 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_69e11e58662081909ae346ab384514ca completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15f33c9cc819086d098f36e4121dc completed April 29, 2026, 1:30 a.m.
Created at: April 16, 2026, 8:51 p.m.