Triple
T4492424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alnylam Pharmaceuticals |
E100609
|
entity |
| Predicate | collaboratesWith |
P37
|
FINISHED |
| Object | Sanofi |
E133715
|
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: Sanofi | Statement: [Alnylam Pharmaceuticals, collaboratesWith, Sanofi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanofi Context triple: [Alnylam Pharmaceuticals, collaboratesWith, Sanofi]
-
A.
Sanofi
chosen
Sanofi is a major French multinational pharmaceutical company known for developing prescription medicines, vaccines, and consumer healthcare products worldwide.
-
B.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
C.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
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_69bd43cdf15081909a4fa2585ff63b3e |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd556f99b48190ae60506a35b43c29 |
completed | March 20, 2026, 2:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd6f8190e88190aec651ac9fe9ef92 |
completed | March 20, 2026, 4:02 p.m. |
Created at: March 20, 2026, 12:59 p.m.