Triple
T30831256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valtiberina Toscana |
E785224
|
entity |
| Predicate | culturalRoute |
P34131
|
FINISHED |
| Object | Via di Francesco |
—
|
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: Via di Francesco | Statement: [Valtiberina Toscana, culturalRoute, Via di Francesco]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: culturalRoute Context triple: [Valtiberina Toscana, culturalRoute, Via di Francesco]
-
A.
hasCulturalRoute
chosen
Indicates that one entity is connected to another by a designated cultural route, such as a path, itinerary, or network highlighting cultural heritage or traditions.
-
B.
nearCulturalRoute
Indicates that one entity is located close to a designated cultural route or pathway.
-
C.
culturalGroupsAlongRoute
Indicates the cultural groups that are present or encountered along a specified route.
-
D.
culturalType
Indicates the classification of something according to its cultural category, style, or tradition.
-
E.
culturalInterest
Indicates a relationship where one entity has an interest in, appreciation for, or engagement with the culture, traditions, or cultural expressions associated with another 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_69f224b73d8c81908129383bfb397c87 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f7516d5b4081908588a6feb541f355 |
completed | May 3, 2026, 1:45 p.m. |
| PD | Predicate disambiguation | batch_69f74d40ebb081909daf60623e38f41d |
completed | May 3, 2026, 1:27 p.m. |
Created at: April 29, 2026, 8:44 p.m.