Triple
T6968963
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dasara in Mysuru |
E161554
|
entity |
| Predicate | touristAttendance |
P12597
|
FINISHED |
| Object | Hundreds of thousands of visitors annually |
—
|
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: Hundreds of thousands of visitors annually | Statement: [Dasara in Mysuru, touristAttendance, Hundreds of thousands of visitors annually]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristAttendance Context triple: [Dasara in Mysuru, touristAttendance, Hundreds of thousands of visitors annually]
-
A.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
-
B.
hasTouristVisits
Indicates that one entity experiences or records visits from tourists to another entity.
-
C.
touristAccess
Indicates that a place or resource is available for use or visitation by tourists.
-
D.
touristArrivalsPerYearApprox
chosen
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
E.
shareTourismFlows
Indicates that two places are connected by or exchange significant tourism flows, such as visitors or tourist traffic, between them.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db152b2081909271493a5d1469fb |
completed | March 27, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.