Triple

T16252920
Position Surface form Disambiguated ID Type / Status
Subject Khao Lak E394556 entity
Predicate accessibleFrom P1985 FINISHED
Object Phuket E93356 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: Phuket | Statement: [Khao Lak, accessibleFrom, Phuket]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phuket
Context triple: [Khao Lak, accessibleFrom, Phuket]
  • A. Phuket chosen
    Phuket is Thailand’s largest island and a major beach and resort destination in the Andaman Sea, renowned for its vibrant nightlife, coastal scenery, and role as a hub for cultural festivals and tourism.
  • B. 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.
  • C. Krabi
    Krabi is a coastal province in southern Thailand renowned for its dramatic limestone cliffs, clear turquoise waters, and island-hopping beaches like Railay and the Phi Phi Islands.
  • D. Pattaya
    Pattaya is a major Thai coastal city known for its vibrant nightlife, beaches, and role as a leading international tourist resort.
  • E. Patong Beach
    Patong Beach is Phuket’s most famous resort area, known for its long sandy shoreline, vibrant nightlife, and dense concentration of hotels, bars, and restaurants.
  • 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_69d87f2171208190951025e526947816 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e24597b74481908fdb8175628a57a1 completed April 17, 2026, 2:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a000ee788f88190b16d267f1eee6d62 completed May 10, 2026, 4:51 a.m.
Created at: April 10, 2026, 5:04 a.m.