Triple

T10095879
Position Surface form Disambiguated ID Type / Status
Subject Technical University of Denmark E215863 entity
Predicate memberOf P10 FINISHED
Object CESAER
CESAER is a European association of leading universities of science and technology that collaborates to advance 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: [Technical University of Denmark, memberOf, CESAER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CESAER
Context triple: [Technical University of Denmark, memberOf, 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. Canace
    Canace is a figure in Greek mythology, traditionally known as a daughter of Aeolus and Enarete and associated with tragic love stories.
  • E. Cisra
    Cisra is the ancient Etruscan city that later became known as Cerveteri in central Italy.
  • 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: [Technical University of Denmark, memberOf, CESAER]
Generated description
CESAER is a European association of leading universities of science and technology that collaborates to advance 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 that collaborates to advance 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. Canace
    Canace is a figure in Greek mythology, traditionally known as a daughter of Aeolus and Enarete and associated with tragic love stories.
  • 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_69ca83a4947c8190823a7495dc5d96ed completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd0798c248190af675e30e280daa8 completed April 2, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b6b8d604819094db099981219e72 completed April 5, 2026, 7:23 p.m.
NEDg Description generation batch_69d2b8a728bc8190b9baf93a40c00642 completed April 5, 2026, 7:31 p.m.
NED2 Entity disambiguation (via description) batch_69d2b90a1de88190b7c8cf6356ffc376 completed April 5, 2026, 7:33 p.m.
Created at: March 30, 2026, 9:02 p.m.