Triple
T4741125
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Forks, Washington |
E105242
|
entity |
| Predicate | tourismEvent |
P58503
|
FINISHED |
| Object | Twilight-themed festivals and events |
—
|
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: Twilight-themed festivals and events | Statement: [Forks, Washington, tourismEvent, Twilight-themed festivals and events]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismEvent Context triple: [Forks, Washington, tourismEvent, Twilight-themed festivals and events]
-
A.
tourismTheme
Indicates the main subject or focus of a tourism-related activity, service, or destination (such as cultural, adventure, or eco-tourism).
-
B.
tourismFeature
Indicates that something serves as an attraction, amenity, or point of interest relevant to tourism or visitors.
-
C.
seasonalTourism
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
D.
tourismBoom
Indicates a rapid and significant increase in tourism activity, such as visitor numbers, spending, or development, within a particular place or period.
-
E.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
- 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_69bd43ef87a48190a5bc3600711aa032 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd64a5f3548190a6acf1dcfd64d11d |
completed | March 20, 2026, 3:15 p.m. |
| PD | Predicate disambiguation | batch_69bd6221c3b881908604f35f8de6f16b |
completed | March 20, 2026, 3:05 p.m. |
| PDg | Predicate description generation | batch_69bd637e19e081909e415544106c7835 |
completed | March 20, 2026, 3:10 p.m. |
Created at: March 20, 2026, 1:19 p.m.