Triple
T22590135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circuito de Playas |
E564920
|
entity |
| Predicate | hasSeasonalTrafficIncrease |
P127382
|
FINISHED |
| Object | summer months in Lima |
—
|
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: summer months in Lima | Statement: [Circuito de Playas, hasSeasonalTrafficIncrease, summer months in Lima]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeasonalTrafficIncrease Context triple: [Circuito de Playas, hasSeasonalTrafficIncrease, summer months in Lima]
-
A.
servesSeasonalTraffic
Indicates that an entity provides service only during specific seasons or periods of the year, rather than year-round.
-
B.
hasPeakVisitationSeason
chosen
Indicates that an entity experiences its highest or most concentrated level of visitation during a specific season or time period.
-
C.
hasHeavyTraffic
Indicates that a location, route, or area is experiencing a high volume of traffic, causing congestion or delays.
-
D.
hasHeavyPassengerTraffic
Indicates that an entity experiences a high volume of passenger movement or usage over a given period.
-
E.
hasSeasonalMigration
Indicates that an entity regularly moves between different locations according to seasonal or cyclical environmental changes.
- 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_69e245836014819091b91ed3074742a3 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1615f63788190acf776b313f0794a |
completed | April 29, 2026, 1:39 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
Created at: April 17, 2026, 2:48 p.m.