Triple

T7968014
Position Surface form Disambiguated ID Type / Status
Subject Guangzhou Baiyun International Airport E185253 entity
Predicate hasICAOCode P419 FINISHED
Object ZGGG E185254 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: ZGGG | Statement: [Guangzhou Baiyun International Airport, hasICAOCode, ZGGG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ZGGG
Context triple: [Guangzhou Baiyun International Airport, hasICAOCode, ZGGG]
  • A. ZGGG chosen
    ZGGG is the ICAO airport code for Guangzhou Baiyun International Airport, a major aviation hub serving Guangzhou in southern China.
  • B. ZGHA
    ZGHA is the ICAO airport code for Changsha Huanghua International Airport, a major air transport hub serving Changsha and the Hunan province in China.
  • C. ZG
    ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
  • D. GZT
    GZT is the IATA airport code for Oğuzeli Airport serving Gaziantep in southeastern Turkey.
  • E. ZPPP
    ZPPP is the ICAO airport code for Kunming Changshui International Airport, a major aviation hub in Yunnan Province, China.
  • 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_69ca8297699481909b75a405f01e03af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3bd06ee081908c5080003fb7b8f7 completed March 31, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe0a334e08190ae67a9ca7b51a128 completed March 31, 2026, 2:56 p.m.
Created at: March 30, 2026, 5:13 p.m.