Triple

T6918147
Position Surface form Disambiguated ID Type / Status
Subject Technical University of Darmstadt E160115 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 Darmstadt, memberOf, CESAER]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CESAER
Context triple: [Technical University of Darmstadt, 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. 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: [Technical University of Darmstadt, 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. 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_69c6883ab1008190a07129ff06f625d9 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6d9e17ea08190b8c4142af8adfba0 completed March 27, 2026, 7:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7512b39b081908370c43ed3d65829 completed March 28, 2026, 3:55 a.m.
NEDg Description generation batch_69c7523737e48190b2ad3e7bea02878d completed March 28, 2026, 3:59 a.m.
NED2 Entity disambiguation (via description) batch_69c752eb3e1c8190bad35727e573c41f completed March 28, 2026, 4:02 a.m.
Created at: March 27, 2026, 2:26 p.m.