Triple
T16218511
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Embassy of Spain to the Holy See |
E393656
|
entity |
| Predicate | typeOfRelation |
P10690
|
FINISHED |
| Object | bilateral relations |
—
|
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: bilateral relations | Statement: [Embassy of Spain to the Holy See, typeOfRelation, bilateral relations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfRelation Context triple: [Embassy of Spain to the Holy See, typeOfRelation, bilateral relations]
-
A.
relationshipType
chosen
Indicates the specific kind of relationship that exists between two or more entities.
-
B.
valueRelation
Indicates a comparative or associative relationship between the values or magnitudes of two or more entities.
-
C.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
-
D.
subjectRelation
Indicates that one entity stands in a specified relational role or connection to another entity.
-
E.
datumRelation
Indicates a relationship where one piece of data is connected to, derived from, or otherwise associated with another piece of data.
- 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.