Triple

T27070096
Position Surface form Disambiguated ID Type / Status
Subject Nan province E685299 entity
Predicate hasNationalPark P105 FINISHED
Object Mae Charim National Park
Mae Charim National Park is a protected mountainous forest area in northern Thailand known for its rich biodiversity, scenic rivers, and opportunities for trekking and white-water rafting.
 E1764731 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: Mae Charim National Park | Statement: [Nan province, hasNationalPark, Mae Charim National Park]
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: Mae Charim National Park
Triple: [Nan province, hasNationalPark, Mae Charim National Park]
Generated description
Mae Charim National Park is a protected mountainous forest area in northern Thailand known for its rich biodiversity, scenic rivers, and opportunities for trekking and white-water rafting.

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_69ef14835fcc81908bd737b4267ae528 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f623107cf08190af1660cd6987c912 completed May 2, 2026, 4:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a126256faa08190bd75cfa4f625f162 completed May 24, 2026, 2:28 a.m.
NEDg Description generation batch_6a126b3902e8819097ba5dccac464901 completed May 24, 2026, 3:06 a.m.
NED2 Entity disambiguation (via description) batch_6a126bae552081909819ca98031af9e1 completed May 24, 2026, 3:08 a.m.
Created at: April 27, 2026, 8:27 a.m.