Triple
T839864
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phillip A. Sharp |
E18151
|
entity |
| Predicate | coFounded |
P104
|
FINISHED |
| Object | Biogen |
E3807
|
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: Biogen | Statement: [Phillip A. Sharp, coFounded, Biogen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biogen Context triple: [Phillip A. Sharp, coFounded, Biogen]
-
A.
Biogen
chosen
Biogen is a major American biotechnology company known for developing therapies for neurological and neurodegenerative diseases.
-
B.
Eisai
Eisai is a Japanese pharmaceutical company known for developing treatments in neurology and oncology, including Alzheimer’s disease therapies.
-
C.
Eli Lilly and Company
Eli Lilly and Company is a major American pharmaceutical corporation known for developing and manufacturing a wide range of prescription medicines, including treatments for diabetes, cancer, and mental health disorders.
-
D.
Genentech
Genentech is a pioneering American biotechnology company known for developing groundbreaking therapies and being one of the first firms to apply genetic engineering to medicine.
-
E.
Abbott Laboratories
Abbott Laboratories is a global healthcare company that develops and manufactures medical devices, diagnostics, branded generic medicines, and nutritional products.
- 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_69a49389f44881909a608fb27d89f247 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4abe4ab1081909207ae2eec1898d9 |
completed | March 1, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7929860f081909c86f84d7cfe6acb |
completed | March 4, 2026, 2:02 a.m. |
Created at: March 1, 2026, 7:38 p.m.