Triple

T5506580
Position Surface form Disambiguated ID Type / Status
Subject Kevin Roche E144454 entity
Predicate familyName P18 FINISHED
Object Roche
Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
 E533363 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: Roche | Statement: [Kevin Roche, familyName, Roche]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Roche
Context triple: [Kevin Roche, familyName, Roche]
  • A. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • B. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • C. Sanofi
    Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
  • D. Schering
    Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
  • E. Bayer
    Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
  • 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: Roche
Triple: [Kevin Roche, familyName, Roche]
Generated description
Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Roche
Target entity description: Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
  • A. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • B. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • C. Sanofi
    Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
  • D. Schering
    Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
  • E. Bayer
    Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
  • 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_69c008f6b5048190a09064116062cf69 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f47d2dc8190ad874be6902d8a4c completed March 22, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69c027b7ef2481908b43f34721b8a8b3 completed March 22, 2026, 5:32 p.m.
NEDg Description generation batch_69c03d668fe88190a1cf88b0708b405f completed March 22, 2026, 7:05 p.m.
NED2 Entity disambiguation (via description) batch_69c03de30928819094af492af0281130 completed March 22, 2026, 7:07 p.m.
Created at: March 22, 2026, 3:32 p.m.