Triple
T25682605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Novena to Our Lady of Lourdes |
E643980
|
entity |
| Predicate | practicedOver |
P159182
|
FINISHED |
| Object | nine consecutive days |
—
|
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: nine consecutive days | Statement: [Novena to Our Lady of Lourdes, practicedOver, nine consecutive days]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: practicedOver Context triple: [Novena to Our Lady of Lourdes, practicedOver, nine consecutive days]
-
A.
practicedWith
Indicates that one entity engaged in practice or training together with another entity.
-
B.
practicedSince
Indicates that an entity has been continuously or intermittently engaged in a particular practice, activity, or profession starting from a specified point in time.
-
C.
hadPractice
Indicates that an entity engaged in or participated in a practice session or rehearsal.
-
D.
practicedUnder
Indicates that one entity gained experience or training while working under the guidance, supervision, or mentorship of another entity.
-
E.
endedPracticeOf
Indicates that one entity caused another entity to stop or discontinue a particular practice or activity.
- 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_69e77e8046888190b07ffa58c7e2c37a |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f5fb7a1bc48190977f41d62801e190 |
completed | May 2, 2026, 1:26 p.m. |
| PD | Predicate disambiguation | batch_69f4938262ac8190b41f922d0407d272 |
completed | May 1, 2026, 11:50 a.m. |
| PDg | Predicate description generation | batch_69f497b8abb88190bb672cf6907c4b8d |
completed | May 1, 2026, 12:08 p.m. |
Created at: April 21, 2026, 8:03 p.m.