Triple
T9434745
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Railay Beach |
E227474
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Ao Nang |
E181644
|
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: Ao Nang | Statement: [Railay Beach, locatedNear, Ao Nang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ao Nang Context triple: [Railay Beach, locatedNear, Ao Nang]
-
A.
Sanya
Sanya is a major resort city on the southern coast of China’s Hainan Island, known for its tropical climate and popular beach tourism.
-
B.
Ao Nang Beach
chosen
Ao Nang Beach is a popular tourist beach in Krabi, Thailand, known for its scenic limestone cliffs, soft sand, and role as a gateway to nearby islands.
-
C.
Hua Hin
Hua Hin is a popular seaside resort town on the Gulf of Thailand, known for its beaches, royal residences, and relaxed coastal atmosphere.
-
D.
Pattaya
Pattaya is a major Thai coastal city known for its vibrant nightlife, beaches, and role as a leading international tourist resort.
-
E.
Nha Trang
Nha Trang is a coastal resort city in Vietnam renowned for its sandy beaches, scuba diving, and vibrant tourism industry.
- 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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7e62d53c81908055e0967e6cd54d |
completed | April 1, 2026, 8:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1104a002481909ca805893bac61c6 |
completed | April 4, 2026, 1:21 p.m. |
Created at: March 30, 2026, 7:50 p.m.