Triple
T6593884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calle Preciados |
E148428
|
entity |
| Predicate | touristFrequency |
P27025
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Calle Preciados, touristFrequency, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristFrequency Context triple: [Calle Preciados, touristFrequency, high]
-
A.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
-
B.
shareTourismFlows
Indicates that two places are connected by or exchange significant tourism flows, such as visitors or tourist traffic, between them.
-
C.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
D.
seasonalTourism
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
E.
visitorFrequency
chosen
Indicates how often a visitor comes to or interacts with a particular entity or location.
- 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_69c687e7b8688190811ffee72e096468 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acfd17388190bd0bb8b2371e7df1 |
completed | March 27, 2026, 4:14 p.m. |
Created at: March 27, 2026, 1:55 p.m.