Triple
T25515559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sant Monica de la Seu d’Urgell |
E639495
|
entity |
| Predicate | culturalTiesType |
P43530
|
FINISHED |
| Object | twinning agreements |
—
|
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: twinning agreements | Statement: [Sant Monica de la Seu d’Urgell, culturalTiesType, twinning agreements]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culturalTiesType Context triple: [Sant Monica de la Seu d’Urgell, culturalTiesType, twinning agreements]
-
A.
hasCulturalRelation
Indicates a relationship in which entities are connected through shared, influencing, or interacting cultural practices, values, traditions, or expressions.
-
B.
culturalIdentityTiedTo
Indicates that an entity’s cultural identity is closely connected or strongly associated with another specified entity or context.
-
C.
closeCulturalTiesWith
Indicates a relationship where two entities share strong, ongoing cultural connections, influences, or exchanges.
-
D.
culturalType
Indicates the classification of something according to its cultural category, style, or tradition.
-
E.
relatedType
chosen
Indicates that one entity is connected to another through a specified type or category of relationship.
- 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_69e75dbe32e48190a62d749a0ff2a96a |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f6562fd3488190be1acd8c526a28d2 |
completed | May 2, 2026, 7:53 p.m. |
| PD | Predicate disambiguation | batch_69f651a731508190bb0c8c2462eba224 |
completed | May 2, 2026, 7:33 p.m. |
Created at: April 21, 2026, 2:55 p.m.