Triple
T9812323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | aflibercept |
E238303
|
entity |
| Predicate | coDeveloper |
P6901
|
FINISHED |
| Object | Bayer HealthCare |
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 HealthCare | Statement: [aflibercept, coDeveloper, Bayer HealthCare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer HealthCare Context triple: [aflibercept, coDeveloper, Bayer HealthCare]
-
A.
Bristol Myers Squibb
Bristol Myers Squibb is a global biopharmaceutical company known for developing and manufacturing innovative medicines in areas such as oncology, immunology, and cardiovascular disease.
-
B.
Schering
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
-
C.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
D.
Merck & Co.
Merck & Co. is a major American pharmaceutical company known for developing and producing vaccines, oncology drugs, and other innovative medicines.
-
E.
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.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb222ba788190a9085272a3de7852 |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d3dfbac819087d07c35a1776064 |
completed | April 10, 2026, 2:46 p.m. |
Created at: March 30, 2026, 8:30 p.m.