Triple
T4131009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bayer |
E85040
|
entity |
| Predicate | hasDivision |
P35
|
FINISHED |
| Object | Bayer Consumer Health |
E85040
|
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: Bayer Consumer Health | Statement: [Bayer, hasDivision, Bayer Consumer Health]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Consumer Health Context triple: [Bayer, hasDivision, Bayer Consumer Health]
-
A.
Reckitt Benckiser
Reckitt Benckiser is a British multinational consumer goods company best known for its health, hygiene, and home products such as Dettol, Lysol, and Durex.
-
B.
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.
-
C.
Johnson & Johnson
Johnson & Johnson is a multinational healthcare conglomerate best known for its pharmaceuticals, medical devices, and consumer health products.
-
D.
GlaxoSmithKline
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
-
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.
- 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_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_69b576c26d4c81909b8be74855cbd03f |
completed | March 14, 2026, 2:54 p.m. |
Created at: March 9, 2026, 3:42 p.m.