Triple

T7892228
Position Surface form Disambiguated ID Type / Status
Subject Nopparat Thara Beach E183263 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: [Nopparat Thara Beach, locatedNear, Ao Nang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ao Nang
Context triple: [Nopparat Thara 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_69ca828c474c8190a254d6499871eaff completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb39fef2e48190a6282c217c33c57a completed March 31, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63a63fe0819095778a12bf437cf4 completed April 1, 2026, 12:15 a.m.
Created at: March 30, 2026, 5 p.m.