Triple
T25614479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ANDA |
E642121
|
entity |
| Predicate | referenceProductType |
P94111
|
FINISHED |
| Object | reference listed drug |
—
|
LITERAL 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: reference listed drug | Statement: [ANDA, referenceProductType, reference listed drug]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: referenceProductType Context triple: [ANDA, referenceProductType, reference listed drug]
-
A.
referenceProduct
Indicates that one product is used as a reference or baseline in relation to another product.
-
B.
referenceType
chosen
Indicates the specific kind or category of reference relationship that one entity has to another.
-
C.
targetsProducts
Indicates that an action, strategy, or entity is specifically directed toward or focused on certain products.
-
D.
typeOfProducts
Indicates the kinds or categories of products that are associated with or offered by an entity.
-
E.
productRole
Indicates the functional role or purpose that a product has within a broader system, context, or usage scenario.
- F. None of above.
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_69e77e7a96748190b10f2699041e4e43 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 21, 2026, 4:58 p.m.