Triple

T6451849
Position Surface form Disambiguated ID Type / Status
Subject Mergui Archipelago E139885 entity
Predicate accessPoint P1985 FINISHED
Object Ranong E178430 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: Ranong | Statement: [Mergui Archipelago, accessPoint, Ranong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ranong
Context triple: [Mergui Archipelago, accessPoint, Ranong]
  • A. Ranong province chosen
    Ranong province is a coastal province in southern Thailand known for its mountainous rainforest terrain, high rainfall, and location along the Andaman Sea near the Myanmar border.
  • B. Mae Sot
    Mae Sot is a Thai border town in Tak Province known as a major hub for cross-border trade and migration with Myanmar and for its numerous refugee and humanitarian aid organizations.
  • C. Laem Chabang
    Laem Chabang is Thailand’s largest deep-sea commercial port and a major hub for maritime trade in Southeast Asia.
  • D. Tasiwit
    Tasiwit is an alternative name for Siwi, a Berber language spoken in Egypt’s Siwa Oasis.
  • E. Tillangchong
    Tillangchong is a remote, sparsely inhabited island in India’s Nicobar archipelago, noted for its dense tropical forests and rich marine and bird life.
  • 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_69c008b301948190a35854e5284dc822 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c069b62cbc81908b7f1a0dc1ed3e1d completed March 22, 2026, 10:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69c64bd671d08190a24e8d666040bcc2 completed March 27, 2026, 9:20 a.m.
Created at: March 22, 2026, 4:47 p.m.