Triple
T36600412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CLEOPATRA trial |
E902898
|
entity |
| Predicate | randomizationRatio |
P185925
|
FINISHED |
| Object | 1:1 |
—
|
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: 1:1 | Statement: [CLEOPATRA trial, randomizationRatio, 1:1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: randomizationRatio Context triple: [CLEOPATRA trial, randomizationRatio, 1:1]
-
A.
randomizationMethod
Indicates the procedure or technique used to randomly assign subjects or units to different groups or conditions in a study or experiment.
-
B.
isRandomized
Indicates that the selection, ordering, or assignment associated with something is determined by a random process rather than a fixed or predetermined rule.
-
C.
canBeRandomized
Indicates that the entity is capable of having its state, order, or selection determined by a random process.
-
D.
introducesRandomnessIn
Indicates that something adds an element of unpredictability or variability into another process, system, or outcome.
-
E.
alternativeRatio
Indicates the proportion or comparative share between different alternative options within a given context.
- 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_69f76e66b7b88190848f7a3e1188915f |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7c371931c8190afb1d4dd5157f92c |
completed | May 3, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69f7c1baf25c8190a78dd54a400d2c50 |
completed | May 3, 2026, 9:44 p.m. |
| PDg | Predicate description generation | batch_69f7c3705b5c81908c84004543a71c07 |
completed | May 3, 2026, 9:51 p.m. |
Created at: May 3, 2026, 4:11 p.m.