Triple
T16218579
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spain–Holy See relations |
E393657
|
entity |
| Predicate | includesStateVisit |
P37603
|
FINISHED |
| Object | papal visits to Spain |
—
|
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: papal visits to Spain | Statement: [Spain–Holy See relations, includesStateVisit, papal visits to Spain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesStateVisit Context triple: [Spain–Holy See relations, includesStateVisit, papal visits to Spain]
-
A.
includesStateOrProvince
Indicates that one entity geographically contains or encompasses a specific state or province within its boundaries.
-
B.
regionsVisited
Indicates that an entity has traveled to, been present in, or otherwise visited specific geographic regions.
-
C.
hasVisitation
chosen
Indicates that one entity visits, or is allowed or scheduled to visit, another entity or location.
-
D.
visitedFor
Indicates that one entity traveled to or attended another entity (such as a place, person, or event) for a specific purpose or reason.
-
E.
passesThroughState
Indicates that something (such as a route, path, or process) traverses or goes through a particular state or region during its course.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e227f95de081908e1abe32d281dbe7 |
completed | April 17, 2026, 12:30 p.m. |
| PD | Predicate disambiguation | batch_69e219e94a448190b73a4e6aa374eb4a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.