Triple

T1650356
Position Surface form Disambiguated ID Type / Status
Subject Guangzhou Railway Station E35676 entity
Predicate hasStationCode P1289 FINISHED
Object GZQ
GZQ is the station code used to identify Guangzhou Railway Station, a major rail transport hub in Guangzhou, China.
 E185770 NE FINISHED

How this triple was built (4 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: GZQ | Statement: [Guangzhou Railway Station, hasStationCode, GZQ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GZQ
Context triple: [Guangzhou Railway Station, hasStationCode, GZQ]
  • A. ZG
    ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
  • B. QGM
    QGM is the post-nominal letters used to denote recipients of The Queen's Gallantry Medal, a British decoration awarded for exemplary acts of bravery.
  • C. YQG
    YQG is the IATA airport code for Windsor International Airport, a commercial airport serving Windsor, Ontario, Canada and the surrounding region.
  • D. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • E. ZGGG
    ZGGG is the ICAO airport code for Guangzhou Baiyun International Airport, a major aviation hub serving Guangzhou in southern China.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: GZQ
Triple: [Guangzhou Railway Station, hasStationCode, GZQ]
Generated description
GZQ is the station code used to identify Guangzhou Railway Station, a major rail transport hub in Guangzhou, China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GZQ
Target entity description: GZQ is the station code used to identify Guangzhou Railway Station, a major rail transport hub in Guangzhou, China.
  • A. ZG
    ZG is the vehicle registration code used on license plates for the city of Zagreb, the capital of Croatia.
  • B. QGM
    QGM is the post-nominal letters used to denote recipients of The Queen's Gallantry Medal, a British decoration awarded for exemplary acts of bravery.
  • C. YQG
    YQG is the IATA airport code for Windsor International Airport, a commercial airport serving Windsor, Ontario, Canada and the surrounding region.
  • D. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • E. ZGGG
    ZGGG is the ICAO airport code for Guangzhou Baiyun International Airport, a major aviation hub serving Guangzhou in southern China.
  • F. None of above. chosen

Provenance (5 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_69a8860568888190a32cd9f70acbba42 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a66b58c819082d38ef1c805cf44 completed March 5, 2026, 4:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad60a996508190bc227400cb7713ac completed March 8, 2026, 11:42 a.m.
NEDg Description generation batch_69ad61323b308190b883c4bf2c3ca1bf completed March 8, 2026, 11:44 a.m.
NED2 Entity disambiguation (via description) batch_69ad622d695481909351a9c80f8d646f completed March 8, 2026, 11:49 a.m.
Created at: March 4, 2026, 7:29 p.m.