Triple
T6535174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Golden Week |
E152340
|
entity |
| Predicate | tourismPattern |
P53625
|
FINISHED |
| Object | outbound tourism peak from Japan |
—
|
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: outbound tourism peak from Japan | Statement: [Golden Week, tourismPattern, outbound tourism peak from Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismPattern Context triple: [Golden Week, tourismPattern, outbound tourism peak from Japan]
-
A.
tourPattern
Indicates a recurring or structured sequence of visits or stops that defines how a tour is organized or carried out.
-
B.
travelPattern
Indicates the typical routes, frequencies, and behaviors associated with how an entity moves or travels between locations.
-
C.
shareTourismFlows
Indicates that two places are connected by or exchange significant tourism flows, such as visitors or tourist traffic, between them.
-
D.
seasonalTourism
chosen
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
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.
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_69c688048ec8819093a47f7d332e12ec |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6adc05da88190b402085954cec8e0 |
completed | March 27, 2026, 4:18 p.m. |
| PD | Predicate disambiguation | batch_69c68abd9c7c819099e4fe8097cd1b28 |
completed | March 27, 2026, 1:48 p.m. |
Created at: March 27, 2026, 1:46 p.m.