Triple

T2748748
Position Surface form Disambiguated ID Type / Status
Subject Juanda International Airport E60932 entity
Predicate ICAOcode P419 FINISHED
Object WARR
WARR is the ICAO airport code for Juanda International Airport, a major airport serving Surabaya in East Java, Indonesia.
 E296225 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: WARR | Statement: [Juanda International Airport, ICAOcode, WARR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: WARR
Context triple: [Juanda International Airport, ICAOcode, WARR]
  • A. WRAN
    WRAN (Wireless Regional Area Network) is a broadband wireless networking standard designed to provide high-speed internet access over large rural and remote areas using unused TV broadcast spectrum.
  • B. WR
    WR is the abbreviation for the German Council of Science and Humanities, a key advisory body that counsels the German federal and state governments on science, research, and higher education policy.
  • C. WR
    WR is the postcode area designation covering Worcester and surrounding parts of Worcestershire in England.
  • D. WR
    WR is the standard abbreviation for World Rugby, the international governing body for the sport of rugby union.
  • E. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • 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: WARR
Triple: [Juanda International Airport, ICAOcode, WARR]
Generated description
WARR is the ICAO airport code for Juanda International Airport, a major airport serving Surabaya in East Java, Indonesia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: WARR
Target entity description: WARR is the ICAO airport code for Juanda International Airport, a major airport serving Surabaya in East Java, Indonesia.
  • A. WRAN
    WRAN (Wireless Regional Area Network) is a broadband wireless networking standard designed to provide high-speed internet access over large rural and remote areas using unused TV broadcast spectrum.
  • B. WR
    WR is the postcode area designation covering Worcester and surrounding parts of Worcestershire in England.
  • C. WR
    WR is the abbreviation for the German Council of Science and Humanities, a key advisory body that counsels the German federal and state governments on science, research, and higher education policy.
  • D. WR
    WR is the standard abbreviation for World Rugby, the international governing body for the sport of rugby union.
  • E. W
    W is one of the iconic white capital letters that make up the famous Hollywood Sign overlooking Los Angeles.
  • 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_69ab4b79846081909096725374d65ce9 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdb517a00819084fd8f8933a25212 completed March 7, 2026, 8:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69afbbd341a88190ae0f5eb94a6fdc92 completed March 10, 2026, 6:36 a.m.
NEDg Description generation batch_69afbd8563248190af593099ecae84ee completed March 10, 2026, 6:43 a.m.
NED2 Entity disambiguation (via description) batch_69afbdb28a708190bbd4bc6632b8e137 completed March 10, 2026, 6:44 a.m.
Created at: March 6, 2026, 9:56 p.m.