Triple
T33351874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Our Lady of Luján |
E853963
|
entity |
| Predicate | hasNovena |
P203619
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Our Lady of Luján, hasNovena, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNovena Context triple: [Our Lady of Luján, hasNovena, yes]
-
A.
hasDevata
Indicates a relationship in which something is associated with, presided over by, or dedicated to a particular deity or divine being.
-
B.
hasSept
Indicates that one entity possesses, contains, or is associated with a sept (a subdivision or clan group) in relation to another entity.
-
C.
hasAgnusDei
Indicates that one entity possesses or includes an Agnus Dei (a representation or object symbolizing the Lamb of God).
-
D.
hasPatronSaint
Indicates that one entity serves as the patron saint associated with, protecting, or representing another entity.
-
E.
hasPrayerRite
Indicates that one entity is associated with, follows, or practices a particular prayer rite or liturgical form.
- 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_69f3496acbc8819099fd0305ecc42080 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a01b3af7b908190b4675c85d32d106c |
completed | May 11, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_6a01b35813c081908e484b2b9ca5dd05 |
completed | May 11, 2026, 10:45 a.m. |
| PDg | Predicate description generation | batch_6a01b3ae7f948190b93fbe0add0dbcec |
completed | May 11, 2026, 10:47 a.m. |
Created at: May 1, 2026, 1:34 a.m.