Triple
T5298280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Ciudad Blanca |
E119908
|
entity |
| Predicate | tourismImage |
P63888
|
FINISHED |
| Object | city of white stone under volcanoes |
—
|
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: city of white stone under volcanoes | Statement: [La Ciudad Blanca, tourismImage, city of white stone under volcanoes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismImage Context triple: [La Ciudad Blanca, tourismImage, city of white stone under volcanoes]
-
A.
tourismFeature
Indicates that something serves as an attraction, amenity, or point of interest relevant to tourism or visitors.
-
B.
tourismTheme
Indicates the main subject or focus of a tourism-related activity, service, or destination (such as cultural, adventure, or eco-tourism).
-
C.
tourDocumented
Indicates that a tour has been recorded or documented, typically capturing its details, events, or progression.
-
D.
tourismDraw
Indicates that one entity attracts tourists or visitor interest to another entity or location.
-
E.
tourismBoom
Indicates a rapid and significant increase in tourism activity, such as visitor numbers, spending, or development, within a particular place or period.
- F. None of above. chosen
Provenance (4 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_69bd446f22b88190b6a47fb91c68a3e7 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd8e44e7c881909b241b2fec366038 |
completed | March 20, 2026, 6:13 p.m. |
| PD | Predicate disambiguation | batch_69bd845097ac81909678624c4907fda4 |
completed | March 20, 2026, 5:30 p.m. |
| PDg | Predicate description generation | batch_69bd8e43c4c88190bb72b9bf56c99425 |
completed | March 20, 2026, 6:13 p.m. |
Created at: March 20, 2026, 1:53 p.m.