Triple

T35484158
Position Surface form Disambiguated ID Type / Status
Subject Bueng Kan E1025545 entity
Predicate adjacentTo P224 FINISHED
Object Bolikhamxai Province
Bolikhamxai Province is a central Laotian province along the Mekong River, known for its mountainous terrain, forests, and role as a transport corridor between Laos, Thailand, and Vietnam.
E2167277 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: Bolikhamxai Province | Statement: [Bueng Kan, adjacentTo, Bolikhamxai Province]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bolikhamxai Province
Triple: [Bueng Kan, adjacentTo, Bolikhamxai Province]
Generated description
Bolikhamxai Province is a central Laotian province along the Mekong River, known for its mountainous terrain, forests, and role as a transport corridor between Laos, Thailand, and Vietnam.

Provenance (5 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_69f76dfbcdd881908c7b0b6bc502252b completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f796ee831c81909f7e868789133148 completed May 3, 2026, 6:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38cb7742c48190b33dd4dc19f9b3ed completed June 22, 2026, 5:43 a.m.
NEDg Description generation batch_6a38cc219ad4819081fec325b458005f completed June 22, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_6a38ccf8e8b48190ac2f931ffa6ff800 completed June 22, 2026, 5:49 a.m.
Created at: May 3, 2026, 4:04 p.m.