Triple

T5085264
Position Surface form Disambiguated ID Type / Status
Subject Ernst Schering E114620 entity
Predicate familyName P18 FINISHED
Object Schering
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
 E492162 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: Schering | Statement: [Ernst Schering, familyName, Schering]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Schering
Context triple: [Ernst Schering, familyName, Schering]
  • A. Pharmacia
    Pharmacia was a major pharmaceutical company known for its global drug development and manufacturing operations before ultimately becoming part of Pfizer.
  • B. 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.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • E. Merck & Co.
    Merck & Co. is a major American pharmaceutical company known for developing and producing vaccines, oncology drugs, and other innovative medicines.
  • 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: Schering
Triple: [Ernst Schering, familyName, Schering]
Generated description
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Schering
Target entity description: Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
  • A. Pharmacia
    Pharmacia was a major pharmaceutical company known for its global drug development and manufacturing operations before ultimately becoming part of Pfizer.
  • B. 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.
  • C. Roche
    Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
  • D. Novartis
    Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
  • E. Merck & Co.
    Merck & Co. is a major American pharmaceutical company known for developing and producing vaccines, oncology drugs, and other innovative medicines.
  • 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_69bd443e941881908eb4e8c685b6f656 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd751db4f4819088b998d7af0e6f41 completed March 20, 2026, 4:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb13a8be08190a0f1fc3aec224dde completed March 21, 2026, 2:54 p.m.
NEDg Description generation batch_69beb22ca4148190b599b48c05e5e3da completed March 21, 2026, 2:58 p.m.
NED2 Entity disambiguation (via description) batch_69beb2c80a6881908ad29936da1240d2 completed March 21, 2026, 3:01 p.m.
Created at: March 20, 2026, 1:40 p.m.