Triple

T15687048
Position Surface form Disambiguated ID Type / Status
Subject CEA Grenoble E380227 entity
Predicate partOf P40 FINISHED
Object CEA Tech E380225 NE FINISHED

How this triple was built (2 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: CEA Tech | Statement: [CEA Grenoble, partOf, CEA Tech]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CEA Tech
Context triple: [CEA Grenoble, partOf, CEA Tech]
  • A. CEA Tech chosen
    CEA Tech is the technological research division of France’s Atomic Energy and Alternative Energies Commission (CEA), focused on developing and transferring advanced industrial and digital technologies.
  • B. ContiTech
    ContiTech is a division of Continental AG that specializes in rubber and plastics technology, producing engineered products such as hoses, conveyor belts, and vibration control components for various industries.
  • C. Imtech
    Imtech was a European technical services provider specializing in electrical engineering, ICT, and mechanical services for buildings and industry.
  • D. Silatech
    Silatech is a Qatar-based non-profit organization focused on promoting youth employment and economic opportunities across the Arab world.
  • E. Cegelec
    Cegelec is an international engineering and technology services company specializing in electrical, automation, and information systems for infrastructure and industry.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d86d99e860819094b6957cde470f2c completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e04f4cee5481908699fbb2b7bdd2f6 completed April 16, 2026, 2:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff6ee740e08190aa048b374ea7bfa0 completed May 9, 2026, 5:29 p.m.
Created at: April 10, 2026, 4:44 a.m.