Triple
T19992872
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namenda |
E494105
|
entity |
| Predicate | developedBy |
P73
|
FINISHED |
| Object | Allergan |
—
|
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: Allergan | Statement: [Namenda, developedBy, Allergan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allergan Context triple: [Namenda, developedBy, Allergan]
-
A.
Allergan
chosen
Allergan is a global pharmaceutical company best known for developing branded drugs and medical aesthetics products, including Botox.
-
B.
Alcon
Alcon is a global eye care company specializing in ophthalmic pharmaceuticals, surgical equipment, and vision care products.
-
C.
Bausch + Lomb
Bausch + Lomb is a global eye health company best known for its contact lenses, lens care products, and ophthalmic pharmaceuticals.
-
D.
Johnson & Johnson
Johnson & Johnson is a multinational healthcare conglomerate best known for its pharmaceuticals, medical devices, and consumer health products.
-
E.
Perrigo
Perrigo is a global healthcare company best known for manufacturing and distributing over-the-counter and generic prescription pharmaceuticals, consumer healthcare products, and nutritional items.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fe2036c8190b9f313215ad44e87 |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:31 p.m.