Triple
T25449385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Base A Port Lockroy |
E637728
|
entity |
| Predicate | touristVisitsPerSeason |
P82087
|
FINISHED |
| Object | thousands of visitors |
—
|
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: thousands of visitors | Statement: [Base A Port Lockroy, touristVisitsPerSeason, thousands of visitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristVisitsPerSeason Context triple: [Base A Port Lockroy, touristVisitsPerSeason, thousands of visitors]
-
A.
typicalVisitorsPerSeason
chosen
Indicates the usual number of visitors associated with each season for a given entity or location.
-
B.
seasonalTourism
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
C.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
-
D.
hasPeakVisitationSeason
Indicates that an entity experiences its highest or most concentrated level of visitation during a specific season or time period.
-
E.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
- 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_69e75db7c5048190b8da9cd7eeedb610 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f706326c81909d888eb9c0dcb60e |
completed | May 2, 2026, 1:07 p.m. |
| PD | Predicate disambiguation | batch_69f4a0f7c6008190ae8cee3e71e19b94 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 21, 2026, 2:02 p.m.