Triple
T19773553
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Allergan |
E474948
|
entity |
| Predicate | acquiredBy |
P347
|
FINISHED |
| Object | AbbVie |
—
|
NE NERFINISHED |
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: AbbVie | Statement: [Allergan, acquiredBy, AbbVie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AbbVie Context triple: [Allergan, acquiredBy, AbbVie]
-
A.
AbbVie
chosen
AbbVie is a global biopharmaceutical company known for developing and marketing innovative therapies in areas such as immunology, oncology, and neuroscience.
-
B.
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.
-
C.
Abbott Laboratories
Abbott Laboratories is a global healthcare company that develops and manufactures medical devices, diagnostics, branded generic medicines, and nutritional products.
-
D.
Novartis
Novartis is a global Swiss-based pharmaceutical company known for developing innovative medicines across a wide range of therapeutic areas.
-
E.
Amgen
Amgen is a leading American biopharmaceutical company known for developing innovative therapies in areas such as oncology, nephrology, and inflammatory diseases.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6535e450c8190a2628245ae0d0bd3 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:48 p.m.