Triple
T37023758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basilica of Mary Help of Christians, Turin |
E916290
|
entity |
| Predicate | hasChapelsDedicatedTo |
P95665
|
FINISHED |
| Object | Saint John Bosco |
—
|
NE NERFINISHED |
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: Saint John Bosco | Statement: [Basilica of Mary Help of Christians, Turin, hasChapelsDedicatedTo, Saint John Bosco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasChapelsDedicatedTo Context triple: [Basilica of Mary Help of Christians, Turin, hasChapelsDedicatedTo, Saint John Bosco]
-
A.
hasChapels
Indicates that one entity contains, includes, or is associated with one or more chapels.
-
B.
hasCathedralDedicatedTo
Indicates that one entity possesses or contains a cathedral whose dedication (to a saint, figure, or concept) is specifically associated with another entity.
-
C.
hasChapelHonoring
chosen
Indicates that one entity contains or includes a chapel dedicated to honoring another entity.
-
D.
hasChapelLocation
Indicates that one entity serves as the physical location or site where a chapel associated with another entity is situated.
-
E.
hasChapelCountApprox
Indicates an approximate number of chapels associated with an entity.
- 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_69f76e920dc48190acb6bb7ebc4dffab |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69ff17be6ad48190963206f2619b1b28 |
completed | May 9, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69ff1724ba24819092c928fcbcb286ec |
completed | May 9, 2026, 11:14 a.m. |
Created at: May 3, 2026, 4:14 p.m.