Triple
T31253860
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tie Luo Han |
E796903
|
entity |
| Predicate | typicalInfusionTime |
P165740
|
FINISHED |
| Object | short multiple infusions |
—
|
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: short multiple infusions | Statement: [Tie Luo Han, typicalInfusionTime, short multiple infusions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalInfusionTime Context triple: [Tie Luo Han, typicalInfusionTime, short multiple infusions]
-
A.
typicalInfusionCount
Indicates the usual or standard number of infusions associated with a given treatment or protocol.
-
B.
timeToAdminister
chosen
Indicates the scheduled or required amount of time needed to administer a treatment, procedure, or action to a subject.
-
C.
typicalBoilingTime
Indicates the usual duration required for something to reach or remain at its boiling point under standard conditions.
-
D.
dosingInterval
Indicates the time period that should elapse between consecutive doses of a medication or treatment.
-
E.
brewingTime
Indicates the duration required to brew or prepare a beverage or similar concoction.
- 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_69f224dc84d0819081f1cb6f9127e6b1 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a037c876524819098545e6037d3107d |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379e5174c8190a0bdde7e381b7624 |
completed | May 12, 2026, 7:05 p.m. |
Created at: April 29, 2026, 9:12 p.m.