Triple
T19992946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ebixa |
E494106
|
entity |
| Predicate | developedBy |
P73
|
FINISHED |
| Object | H. Lundbeck A/S |
—
|
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: H. Lundbeck A/S | Statement: [Ebixa, developedBy, H. Lundbeck A/S]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: H. Lundbeck A/S Context triple: [Ebixa, developedBy, H. Lundbeck A/S]
-
A.
Lundbeck
chosen
Lundbeck is a Danish multinational pharmaceutical company specializing in research, development, and marketing of treatments for psychiatric and neurological disorders.
-
B.
Forest Laboratories
Forest Laboratories was an American pharmaceutical company known for developing and marketing branded prescription drugs, particularly in the central nervous system and cardiovascular therapeutic areas.
-
C.
Eisai
Eisai is a Japanese pharmaceutical company known for developing treatments in neurology and oncology, including Alzheimer’s disease therapies.
-
D.
Eisai
Eisai was a Japanese Buddhist monk of the Kamakura period best known for introducing Rinzai Zen and promoting tea culture in Japan.
-
E.
Boehringer Ingelheim
Boehringer Ingelheim is a major German research-driven pharmaceutical company known for developing prescription medicines, animal health products, and biopharmaceuticals worldwide.
- 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.