Triple

T4608944
Position Surface form Disambiguated ID Type / Status
Subject Unified Team E100506 entity
Predicate country P26 FINISHED
Object EUN
EUN was the Olympic country code for the Unified Team of former Soviet republics that competed together at the 1992 Winter and Summer Olympics.
 E456479 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: EUN | Statement: [Unified Team, country, EUN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: EUN
Context triple: [Unified Team, country, EUN]
  • A. EUG
    EUG is the IATA airport code for Mahlon Sweet Field, the primary commercial airport serving Eugene and the surrounding region in western Oregon, United States.
  • B. UNU
    UNU is the United Nations University, a global think tank and postgraduate teaching organization of the UN system focused on research and capacity-building for sustainable development and peace.
  • C. UNÎMES
    UNÎMES is the acronym for the University of Nîmes, a French public higher education and research institution located in Nîmes, in the Occitanie region.
  • D. UN
    The UN is an international organization founded in 1945 that brings together most of the world’s countries to promote peace, security, cooperation, and human rights.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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: EUN
Triple: [Unified Team, country, EUN]
Generated description
EUN was the Olympic country code for the Unified Team of former Soviet republics that competed together at the 1992 Winter and Summer Olympics.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: EUN
Target entity description: EUN was the Olympic country code for the Unified Team of former Soviet republics that competed together at the 1992 Winter and Summer Olympics.
  • A. EUG
    EUG is the IATA airport code for Mahlon Sweet Field, the primary commercial airport serving Eugene and the surrounding region in western Oregon, United States.
  • B. UNU
    UNU is the United Nations University, a global think tank and postgraduate teaching organization of the UN system focused on research and capacity-building for sustainable development and peace.
  • C. UNÎMES
    UNÎMES is the acronym for the University of Nîmes, a French public higher education and research institution located in Nîmes, in the Occitanie region.
  • D. UN
    The UN is an international organization founded in 1945 that brings together most of the world’s countries to promote peace, security, cooperation, and human rights.
  • E. ENA
    ENA is a prestigious French grande école that trained many of the country’s top civil servants and political leaders.
  • 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_69bd43cce1e08190a07d53af6a9b6c24 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd599f08d88190ad4bed8bafb592cd completed March 20, 2026, 2:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa789cdc8190add02a5970f9a0b6 completed March 21, 2026, 1:55 a.m.
NEDg Description generation batch_69bdfb385ccc8190ab8de82c6df645cf completed March 21, 2026, 1:58 a.m.
NED2 Entity disambiguation (via description) batch_69bdfbcdb6d881909feb3a6be81a567f completed March 21, 2026, 2 a.m.
Created at: March 20, 2026, 1:12 p.m.