Triple
T2315814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Imbruvica |
E51059
|
entity |
| Predicate | hasInitialApprovalYearUS |
P7919
|
FINISHED |
| Object | 2013 |
—
|
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: 2013 | Statement: [Imbruvica, hasInitialApprovalYearUS, 2013]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasInitialApprovalYearUS Context triple: [Imbruvica, hasInitialApprovalYearUS, 2013]
-
A.
regulatoryApprovalYear
Indicates the calendar year in which an official regulatory body granted approval for the referenced item, action, or entity.
-
B.
authorizationYear
chosen
Indicates the year in which an official approval, permission, or authorization for something was granted.
-
C.
hasFirstProofYear
Indicates the year in which something was first proven or formally demonstrated to be true.
-
D.
citizenshipGrantedYear
Indicates the specific year in which an entity was officially granted citizenship.
-
E.
approvedInCountry
Indicates that an entity has received official authorization or approval within the jurisdiction of a specified country.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc685f05481909c863b29d1f6bacd |
completed | March 7, 2026, 6:32 a.m. |
| PD | Predicate disambiguation | batch_69abc58e88e481908733fdf79d3f8a15 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.