Triple
T13018853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Batu City |
E322622
|
entity |
| Predicate | tourismTargetMarket |
P65464
|
FINISHED |
| Object | domestic 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: domestic tourists | Statement: [Batu City, tourismTargetMarket, domestic tourists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: tourismTargetMarket Context triple: [Batu City, tourismTargetMarket, domestic tourists]
-
A.
tourismMarket
chosen
Indicates a relationship where a location, service, or product functions as a destination or offering within the travel and tourism economy, attracting and serving tourists as a market segment.
-
B.
tourismTrend
Indicates how patterns or levels of tourism activity change over time or across locations.
-
C.
travelMarket
Indicates a relationship where an entity participates in or is associated with the commercial exchange, promotion, or sale of travel-related services or experiences.
-
D.
tourismType
Indicates the specific category or kind of tourism activity or experience associated with an entity.
-
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_69d807657e8c8190bd9435ee2f823845 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d97ece22908190a0941e23df7c774d |
completed | April 10, 2026, 10:50 p.m. |
| PD | Predicate disambiguation | batch_69d97dc39a0881908119c62e31bf6182 |
completed | April 10, 2026, 10:46 p.m. |
Created at: April 9, 2026, 8:51 p.m.