Triple
T21660383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kato-Katz thick smear |
E534577
|
entity |
| Predicate | readingTime |
P144899
|
FINISHED |
| Object | typically 30 minutes after preparation for Schistosoma eggs |
—
|
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: typically 30 minutes after preparation for Schistosoma eggs | Statement: [Kato-Katz thick smear, readingTime, typically 30 minutes after preparation for Schistosoma eggs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: readingTime Context triple: [Kato-Katz thick smear, readingTime, typically 30 minutes after preparation for Schistosoma eggs]
-
A.
timeRequiredToRead
Indicates the amount of time needed for an entity (such as a person) to read a given item or content.
-
B.
reading
Indicates that an entity is engaged in the activity of interpreting and understanding written or printed material from another entity or source.
-
C.
readSpeed
Indicates the rate at which an entity reads or processes written material.
-
D.
readingAid
Indicates that one entity assists or facilitates another entity’s ability to read or engage in reading activities.
-
E.
readsTo
Indicates that one entity reads or recites content aloud for the benefit of another entity.
- 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c07bcb88190a9864672c20325ff |
completed | April 27, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69e696826c3c81909270791e79760937 |
completed | April 20, 2026, 9:11 p.m. |
| PDg | Predicate description generation | batch_69e69b4aa2b48190830107391e81571a |
completed | April 20, 2026, 9:31 p.m. |
Created at: April 16, 2026, 6:36 p.m.