Triple

T780679
Position Surface form Disambiguated ID Type / Status
Subject Uzbekistan E16489 entity
Predicate ISO3166-1Alpha2 P189 FINISHED
Object UZ
UZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Uzbekistan.
 E92058 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: UZ | Statement: [Uzbekistan, ISO3166-1Alpha2, UZ]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UZ
Context triple: [Uzbekistan, ISO3166-1Alpha2, UZ]
  • A. USZ
    USZ is a major public teaching hospital in Zurich, Switzerland, affiliated with the University of Zurich and known for its advanced medical care and research.
  • B. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • C. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • D. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • E. UL
    UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
  • 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: UZ
Triple: [Uzbekistan, ISO3166-1Alpha2, UZ]
Generated description
UZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Uzbekistan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UZ
Target entity description: UZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Uzbekistan.
  • A. USZ
    USZ is a major public teaching hospital in Zurich, Switzerland, affiliated with the University of Zurich and known for its advanced medical care and research.
  • B. VZ
    VZ is the stock ticker symbol for Verizon Communications Inc., a major U.S.-based telecommunications company providing wireless, internet, and related services.
  • C. KZ
    KZ is the two-letter ISO 3166-1 alpha-2 country code assigned to Kazakhstan for international standardization and identification.
  • D. UL
    UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
  • E. UL
    UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
  • 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_69a4936ad1fc81908f190208059ccf78 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a751ea3481908a622d5255249883 completed March 1, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69a66d9dd9b48190ba7fae75db01c114 completed March 3, 2026, 5:11 a.m.
NEDg Description generation batch_69a66f03f1e88190a2a7cdbe57f57bc2 completed March 3, 2026, 5:17 a.m.
NED2 Entity disambiguation (via description) batch_69a66fa5123c819085ee7cef0c7013c9 completed March 3, 2026, 5:20 a.m.
Created at: March 1, 2026, 7:37 p.m.