Triple

T7968029
Position Surface form Disambiguated ID Type / Status
Subject Guangzhou Baiyun International Airport E185253 entity
Predicate rankedByPassengerTraffic P25678 FINISHED
Object one of the busiest airports in China LITERAL 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: one of the busiest airports in China | Statement: [Guangzhou Baiyun International Airport, rankedByPassengerTraffic, one of the busiest airports in China]
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: rankedByPassengerTraffic
Context triple: [Guangzhou Baiyun International Airport, rankedByPassengerTraffic, one of the busiest airports in China]
  • A. passengerTrafficRankingWorld
    Indicates the relative position of an entity in a global ranking based on the volume of passenger traffic it handles.
  • B. peakPassengerTrafficRank
    Indicates the relative position of an entity in an ordered list based on the amount of passenger traffic it experiences at its peak.
  • C. airportRank
    Indicates the relative position or level assigned to an airport within a ranking or ordered list.
  • D. rankingByAircraftMovements
    Indicates the relative order of entities based on the number of aircraft movements (takeoffs and landings) they handle.
  • E. hasPassengerTrafficRank chosen
    Indicates the relative position or ranking of an entity based on the volume of passenger traffic it handles compared to others.
  • F. None of above.

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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bd06ee081908c5080003fb7b8f7 completed March 31, 2026, 3:13 a.m.
PD Predicate disambiguation batch_69cb047a8e4c81909b79e0f0bf56440c completed March 30, 2026, 11:17 p.m.
Created at: March 30, 2026, 5:13 p.m.