Triple
T31257867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shui Xian |
E797029
|
entity |
| Predicate | infusionCount |
P123836
|
FINISHED |
| Object | multiple infusions possible |
—
|
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: multiple infusions possible | Statement: [Shui Xian, infusionCount, multiple infusions possible]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: infusionCount Context triple: [Shui Xian, infusionCount, multiple infusions possible]
-
A.
typicalInfusionCount
chosen
Indicates the usual or standard number of infusions associated with a given treatment or protocol.
-
B.
bloodUsedFor
Indicates that blood is utilized or applied for a particular purpose, function, or process.
-
C.
typicalDoseCount
Indicates the usual number of doses administered or taken in a standard course of use.
-
D.
hasNumberOfDrops
Indicates the quantity or count of drops associated with an entity or event.
-
E.
usualMonthsAdministered
Indicates the months in which something, typically a treatment or intervention, is normally administered.
- 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_69f224dd5fdc81908a4cd24917b67668 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69edbb7648190bd89c57e0932eac1 |
completed | May 3, 2026, 1:03 a.m. |
| PD | Predicate disambiguation | batch_69f69d1bf8cc8190a78dfa5ab00daf3a |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 9:12 p.m.