Triple
T16434424
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bangla Road |
E399145
|
entity |
| Predicate | touristDemographic |
P94260
|
FINISHED |
| Object | international tourists |
—
|
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: international tourists | Statement: [Bangla Road, touristDemographic, international tourists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: touristDemographic Context triple: [Bangla Road, touristDemographic, international tourists]
-
A.
touristArrivalsPerYearApprox
Indicates an approximate count of how many tourists arrive at a place over the course of a year.
-
B.
touristArrivalsShareInTerritory
Indicates the proportion of total tourist arrivals that occur within a specific territory relative to a larger reference area or total.
-
C.
tourismTrend
Indicates how patterns or levels of tourism activity change over time or across locations.
-
D.
hasTouristProfile
Indicates that an entity possesses characteristics, data, or attributes defining it as a tourist or related to tourism behavior.
-
E.
demographicsIn
chosen
Indicates that demographic information is associated with or applies within a specified geographic or organizational area.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32ba1023481909588aa6a3c677886 |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:10 a.m.