Triple
T1169595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ionis Pharmaceuticals |
E24882
|
entity |
| Predicate | hasCollaborationWith |
P398
|
FINISHED |
| Object | Roche |
E46707
|
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: Roche | Statement: [Ionis Pharmaceuticals, hasCollaborationWith, Roche]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Roche Context triple: [Ionis Pharmaceuticals, hasCollaborationWith, Roche]
-
A.
Roche
chosen
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
B.
Sanofi
Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
-
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.
Pfizer
Pfizer is a major American multinational pharmaceutical and biotechnology corporation known for developing a wide range of prescription medicines and vaccines, including one of the first widely used COVID-19 vaccines.
-
E.
AstraZeneca
AstraZeneca is a global biopharmaceutical company known for researching, developing, and manufacturing prescription medicines across areas such as oncology, cardiovascular, respiratory, and immunology.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bf13ab648190931dea78202096e4 |
completed | March 1, 2026, 10:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac8311ba6481908aaca4c1e9d8b78f |
completed | March 7, 2026, 7:57 p.m. |
Created at: March 1, 2026, 7:45 p.m.