Triple
T4131023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayer |
E85040
|
entity |
| Predicate | hasSubsidiary |
P254
|
FINISHED |
| Object |
Bayer HealthCare
Bayer HealthCare is the healthcare division of Bayer AG, focused on developing and marketing pharmaceutical and medical products worldwide.
|
E85040
|
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: Bayer HealthCare | Statement: [Bayer, hasSubsidiary, Bayer HealthCare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer HealthCare Context triple: [Bayer, hasSubsidiary, Bayer HealthCare]
-
A.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
B.
Merck & Co.
Merck & Co. is a major American pharmaceutical company known for developing and producing vaccines, oncology drugs, and other innovative medicines.
-
C.
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.
-
D.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
E.
GlaxoSmithKline
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
- 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: Bayer HealthCare Triple: [Bayer, hasSubsidiary, Bayer HealthCare]
Generated description
Bayer HealthCare is the healthcare division of Bayer AG, focused on developing and marketing pharmaceutical and medical products worldwide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bayer HealthCare Target entity description: Bayer HealthCare is the healthcare division of Bayer AG, focused on developing and marketing pharmaceutical and medical products worldwide.
-
A.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
B.
Merck & Co.
Merck & Co. is a major American pharmaceutical company known for developing and producing vaccines, oncology drugs, and other innovative medicines.
-
C.
Bayer
chosen
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
-
D.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
E.
GlaxoSmithKline
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
- 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_69aed935ccd881909dc61f81bcdb7a78 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af021f6a508190b8ac1e0d8b859f74 |
completed | March 9, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589de64fc81909662197fdbe45446 |
completed | March 14, 2026, 4:16 p.m. |
| NEDg | Description generation | batch_69b58ad68af88190863ea23e170ce2ca |
completed | March 14, 2026, 4:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b58ba2edbc81908bfbe5d91daf5ae5 |
completed | March 14, 2026, 4:24 p.m. |
Created at: March 9, 2026, 3:42 p.m.