Triple
T6003746
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Loxo Oncology |
E133655
|
entity |
| Predicate | notableCollaboration |
P8554
|
FINISHED |
| Object | Bayer |
E85040
|
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: Bayer | Statement: [Loxo Oncology, notableCollaboration, Bayer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Context triple: [Loxo Oncology, notableCollaboration, Bayer]
-
A.
Bayer
chosen
Bayer is a major German multinational pharmaceutical and life sciences company known for products such as aspirin and its work in healthcare and agriculture.
-
B.
Schering
Schering is a German surname most notably associated with Ernst Schering, a 19th-century pharmacist and founder of the pharmaceutical company Schering AG.
-
C.
Roche
Roche is a common surname of French origin borne by various notable individuals across fields such as architecture, politics, and the arts.
-
D.
Roche
Roche is a major Swiss multinational healthcare company and one of the world’s leading pharmaceutical and diagnostics firms.
-
E.
Hoechst
Hoechst was a major German chemical and pharmaceutical company that later became part of the conglomerate IG Farben.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f0f63148190826198281dce3713 |
completed | March 22, 2026, 8:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11cdec5608190ad093a09acd32ebf |
completed | March 23, 2026, 10:58 a.m. |
Created at: March 22, 2026, 4:06 p.m.