Triple

T6750076
Position Surface form Disambiguated ID Type / Status
Subject Conference of European Schools for Advanced Engineering Education and Research E154319 entity
Predicate hasAbbreviation P43 FINISHED
Object CESAER
CESAER is a European association of leading universities of science and technology focused on advancing engineering education, research, and innovation.
 E28802 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: CESAER | Statement: [Conference of European Schools for Advanced Engineering Education and Research, hasAbbreviation, CESAER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CESAER
Context triple: [Conference of European Schools for Advanced Engineering Education and Research, hasAbbreviation, CESAER]
  • A. Cesca
    Cesca is a feminine given name, commonly used as a short form of Francesca.
  • B. Chéserex
    Chéserex is a small Swiss municipality in the canton of Vaud, situated near the Jura Mountains above Lake Geneva.
  • C. Cellese
    Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
  • D. Cisra
    Cisra is the ancient Etruscan city that later became known as Cerveteri in central Italy.
  • E. Loria
    Loria is a surname most prominently associated with Jeffrey Loria, an American art dealer and former owner of Major League Baseball’s Miami Marlins.
  • 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: CESAER
Triple: [Conference of European Schools for Advanced Engineering Education and Research, hasAbbreviation, CESAER]
Generated description
CESAER is a European association of leading universities of science and technology focused on advancing engineering education, research, and innovation.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CESAER
Target entity description: CESAER is a European association of leading universities of science and technology focused on advancing engineering education, research, and innovation.
  • A. CESAER chosen
    CESAER is a European association of leading universities of science and technology that collaborates to advance engineering education, research, and innovation.
  • B. Cesca
    Cesca is a feminine given name, commonly used as a short form of Francesca.
  • C. Chéserex
    Chéserex is a small Swiss municipality in the canton of Vaud, situated near the Jura Mountains above Lake Geneva.
  • D. Cellese
    Cellese is a regional dialect of the Franco-Provençal language traditionally spoken in a specific area of the Franco-Provençal linguistic region.
  • E. Cisra
    Cisra is the ancient Etruscan city that later became known as Cerveteri in central Italy.
  • F. None of above.

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_69c6880ef37881909268a5a7299b9293 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1da32108190882949aa329d2b60 completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712a793cc8190b838806151851711 completed March 27, 2026, 11:28 p.m.
NEDg Description generation batch_69c7132017a881909a8f4a8d4635d53f completed March 27, 2026, 11:30 p.m.
NED2 Entity disambiguation (via description) batch_69c715cc0c9c8190aae641eaffa5bd7b completed March 27, 2026, 11:42 p.m.
Created at: March 27, 2026, 2:11 p.m.