Triple
T22630048
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Asgrow |
E558522
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Bayer Crop Science |
—
|
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: Bayer Crop Science | Statement: [Asgrow, associatedWith, Bayer Crop Science]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bayer Crop Science Context triple: [Asgrow, associatedWith, Bayer Crop Science]
-
A.
Corteva
Corteva is an American agricultural chemical and seed company formed as an independent entity after the breakup of DowDuPont.
-
B.
Monsanto
Monsanto is a civil parish located in the municipality of Alcanena in central Portugal.
-
C.
Monsanto Company
Monsanto Company was a major American agrochemical and agricultural biotechnology corporation known for producing controversial products such as Agent Orange and genetically modified seeds.
-
D.
Syngenta Seeds (formerly Royal Sluis)
Syngenta Seeds (formerly Royal Sluis) is a major international seed company known for developing and supplying high-quality vegetable and field crop seeds.
-
E.
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.
- 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_69e245467d9881908d6985bd0db7a1f1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f17008e7648190b243c18067b4efb9 |
completed | April 29, 2026, 2:42 a.m. |
Created at: April 17, 2026, 3:02 p.m.