Triple
T6311224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novartis |
E141505
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Sandoz
Sandoz is a historic Swiss pharmaceutical company best known as a predecessor of Novartis and a major player in generic medicines.
|
E584145
|
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: Sandoz | Statement: [Novartis, foundedBy, Sandoz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sandoz Context triple: [Novartis, foundedBy, Sandoz]
-
A.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
B.
Roche
Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
-
C.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
D.
Schering
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
-
E.
Teva Pharmaceutical Industries
Teva Pharmaceutical Industries is a global pharmaceutical company based in Israel, best known as one of the world’s largest manufacturers of generic medicines and a producer of specialty and over-the-counter drugs.
- 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: Sandoz Triple: [Novartis, foundedBy, Sandoz]
Generated description
Sandoz is a historic Swiss pharmaceutical company best known as a predecessor of Novartis and a major player in generic medicines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sandoz Target entity description: Sandoz is a historic Swiss pharmaceutical company best known as a predecessor of Novartis and a major player in generic medicines.
-
A.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
B.
Roche
Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
-
C.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
D.
Schering
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
-
E.
Teva Pharmaceutical Industries
Teva Pharmaceutical Industries is a global pharmaceutical company based in Israel, best known as one of the world’s largest manufacturers of generic medicines and a producer of specialty and over-the-counter drugs.
- 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_69c008d00efc8190a36c05b4b4a3bf4b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0649d1e048190a3fc7fbce9d2ee57 |
completed | March 22, 2026, 9:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5e461e2ec8190af7198a03edb1ff7 |
completed | March 27, 2026, 1:58 a.m. |
| NEDg | Description generation | batch_69c5e66e8b3c8190bd4cd960b91de473 |
completed | March 27, 2026, 2:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5e6e40c588190898ad952b71e5b11 |
completed | March 27, 2026, 2:09 a.m. |
Created at: March 22, 2026, 4:28 p.m.