Triple

T7489408
Position Surface form Disambiguated ID Type / Status
Subject Mahidol University E176964 entity
Predicate locatedIn P40 FINISHED
Object Bangkok, Thailand 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, Thailand | Statement: [Mahidol University, locatedIn, Bangkok, Thailand]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bangkok, Thailand
Context triple: [Mahidol University, locatedIn, Bangkok, Thailand]
  • 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. Ayutthaya, Thailand
    Ayutthaya, Thailand is a historic city north of Bangkok known for its ancient temple ruins and status as a former capital of the Kingdom of Siam.
  • E. Chiang Mai
    Chiang Mai is a historic city in northern Thailand known for its ancient temples, vibrant night markets, and surrounding mountainous landscapes.
  • 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_69c69f24ac508190bb98fe927c0bd065 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f55abcd481909e42ca857fe46cd1 completed March 27, 2026, 9:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69c84eefbcec8190bef282452aaf5515 completed March 28, 2026, 9:58 p.m.
Created at: March 27, 2026, 3:43 p.m.