Triple
T10692680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michael York |
E252048
|
entity |
| Predicate | hasReceivedTreatmentFor |
P95317
|
FINISHED |
| Object | amyloidosis |
—
|
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: amyloidosis | Statement: [Michael York, hasReceivedTreatmentFor, amyloidosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasReceivedTreatmentFor Context triple: [Michael York, hasReceivedTreatmentFor, amyloidosis]
-
A.
hasCommonTreatment
Indicates that two or more entities share at least one treatment method or therapeutic approach in common.
-
B.
usesTreatment
Indicates that one entity applies or employs a particular treatment or therapeutic method on or for another entity.
-
C.
hasSubsequentTreatment
Indicates that one treatment occurs after and in continuation of another treatment in a temporal sequence.
-
D.
hasTreatmentConsideration
Indicates that a particular factor, condition, or option should be taken into account when planning, selecting, or managing a treatment.
-
E.
treatedAt
Indicates that a person or patient received medical care or treatment at a particular healthcare facility or location.
- F. None of above. chosen
Provenance (4 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd37cf408190a1912b3e0aa096a5 |
completed | April 9, 2026, 1:13 a.m. |
| PD | Predicate disambiguation | batch_69d6dd8cc0788190b4c02a772e4b58b3 |
completed | April 8, 2026, 10:58 p.m. |
| PDg | Predicate description generation | batch_69d6df47899481909ac0e518d94883cb |
completed | April 8, 2026, 11:05 p.m. |
Created at: April 8, 2026, 9:11 p.m.