Triple
T7001921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lion Monument |
E162355
|
entity |
| Predicate | touristVisitSeason |
P13215
|
FINISHED |
| Object | year-round |
—
|
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: year-round | Statement: [Lion Monument, touristVisitSeason, year-round]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristVisitSeason Context triple: [Lion Monument, touristVisitSeason, year-round]
-
A.
seasonalTourism
Indicates that tourism activity in a place varies significantly by season, with distinct peak and off-peak periods.
-
B.
hasTouristVisits
Indicates that one entity experiences or records visits from tourists to another entity.
-
C.
typicalSeasonTiming
chosen
Indicates the usual time period or season during which something normally occurs or is expected to take place.
-
D.
isPeakVacationMonthIn
Indicates that a given month falls within the period of highest typical vacation activity in a specified location or context.
-
E.
affectedSeason
Indicates that one entity has an influence on, or causes a change in, a particular season.
- 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_69c68857ffc08190857dc62cd5253777 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc1115c48190a9363473ae21b6c1 |
completed | March 27, 2026, 7:35 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c67c94819084fdcf0398606027 |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:33 p.m.