Triple

T6459327
Position Surface form Disambiguated ID Type / Status
Subject United States Ambassador to Thailand E142075 entity
Predicate seat P75 FINISHED
Object Bangkok E10237 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: Bangkok | Statement: [United States Ambassador to Thailand, seat, Bangkok]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangkok
Context triple: [United States Ambassador to Thailand, seat, Bangkok]
  • A. Bangkok chosen
    Bangkok is the vibrant capital and largest city of Thailand, known for its bustling street life, ornate temples, and role as a major economic and cultural hub in Southeast Asia.
  • B. Pattaya
    Pattaya is a major Thai coastal city known for its vibrant nightlife, beaches, and role as a leading international tourist resort.
  • C. Hat Yai
    Hat Yai is a major commercial and transportation hub city in southern Thailand, known for its bustling markets and proximity to the Malaysian border.
  • D. Chiang Mai
    Chiang Mai is a historic city in northern Thailand known for its ancient temples, vibrant night markets, and surrounding mountainous landscapes.
  • E. Seoul–Bangkok
    Seoul–Bangkok is a major international air route connecting the capital cities of South Korea and Thailand, popular for both tourism and business travel.
  • 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_69c008d2f91c8190a8178767a35e08fc completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069f347f48190a2b22c5b648b17bd completed March 22, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64be1fad88190af07b7053811e3f2 completed March 27, 2026, 9:20 a.m.
Created at: March 22, 2026, 4:48 p.m.