Triple
T691230
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herbert Bayer |
E13396
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
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.
|
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 | Statement: [Herbert Bayer, familyName, Bayer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Context triple: [Herbert Bayer, familyName, Bayer]
-
A.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
B.
Johnson & Johnson
Johnson & Johnson is a multinational healthcare conglomerate best known for its pharmaceuticals, medical devices, and consumer health products.
-
C.
DuPont
DuPont is a major American chemical company historically known for pioneering materials science innovations and playing a key role in U.S. industrial and wartime production.
-
D.
GlaxoSmithKline
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
-
E.
Eisai
Eisai is a Japanese pharmaceutical company known for developing treatments in neurology and oncology, including Alzheimer’s disease therapies.
- 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 Triple: [Herbert Bayer, familyName, Bayer]
Generated description
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bayer Target entity description: Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
-
A.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
B.
Johnson & Johnson
Johnson & Johnson is a multinational healthcare conglomerate best known for its pharmaceuticals, medical devices, and consumer health products.
-
C.
DuPont
DuPont is a major American chemical company historically known for pioneering materials science innovations and playing a key role in U.S. industrial and wartime production.
-
D.
GlaxoSmithKline
GlaxoSmithKline is a global biopharmaceutical company known for developing and manufacturing prescription medicines, vaccines, and consumer healthcare products.
-
E.
Eisai
Eisai is a Japanese pharmaceutical company known for developing treatments in neurology and oncology, including Alzheimer’s disease therapies.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0aebde88190a49d421477713103 |
completed | March 1, 2026, 8:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5dca5b2688190bfe05a165c753211 |
completed | March 2, 2026, 6:53 p.m. |
| NEDg | Description generation | batch_69a5ddeaef148190bb9877cee15959fd |
completed | March 2, 2026, 6:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5fea9df3481909331e3e382ad81ae |
completed | March 2, 2026, 9:18 p.m. |
Created at: March 1, 2026, 7:36 p.m.