Triple
T5941750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kuta |
E132180
|
entity |
| Predicate | hasAttraction |
P105
|
FINISHED |
| Object | Kuta Beach |
E563101
|
NE 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: Kuta Beach | Statement: [Kuta, hasAttraction, Kuta Beach]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kuta Beach Context triple: [Kuta, hasAttraction, Kuta Beach]
-
A.
Kuta Beach
chosen
Kuta Beach is a famous tourist beach in Bali, Indonesia, known for its surfing waves, vibrant nightlife, and sunset views.
-
B.
Koki Beach
Koki Beach is a scenic red-sand beach on Maui’s Hāna coast, known for its rugged shoreline, strong surf, and views of the nearby ʻAlau Island.
-
C.
Kata Beach
Kata Beach is a popular sandy beach on the southwest coast of Phuket, Thailand, known for its clear waters, relaxed atmosphere, and surfing-friendly waves.
-
D.
Kania Beach
Kania Beach is a small, tranquil seaside spot on the Greek island of Chalki, known for its clear waters and relaxed atmosphere.
-
E.
Kite Beach
Kite Beach is a popular public beach in Dubai known for its water sports, especially kitesurfing, and its views of the Burj Al Arab.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c00869d3308190af89b2453e0f7546 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c039346a7c81908d94081b666e1d79 |
completed | March 22, 2026, 6:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c11cc81b3081908a35c4230eba3f06 |
completed | March 23, 2026, 10:58 a.m. |
Created at: March 22, 2026, 4:01 p.m.