Triple

T28346087
Position Surface form Disambiguated ID Type / Status
Subject Mae Sa Valley E717957 entity
Predicate hasWaterFeature P1094 FINISHED
Object Mae Sa Waterfall
Mae Sa Waterfall is a popular multi-tiered cascade set within a forested national park area near Chiang Mai in northern Thailand, known for its scenic hiking trails and swimming spots.
 E1761562 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 Sa Waterfall | Statement: [Mae Sa Valley, hasWaterFeature, Mae Sa Waterfall]
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 Sa Waterfall
Triple: [Mae Sa Valley, hasWaterFeature, Mae Sa Waterfall]
Generated description
Mae Sa Waterfall is a popular multi-tiered cascade set within a forested national park area near Chiang Mai in northern Thailand, known for its scenic hiking trails and swimming spots.

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_69eff6eb30388190b898b96c4be6f49d completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64c0736d88190a6214ac506ed0fb2 completed May 2, 2026, 7:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16416f1898819081a7d35737de5c2b completed May 27, 2026, 12:57 a.m.
NEDg Description generation batch_6a16439eb0c081909207ac029d9f32b8 completed May 27, 2026, 1:06 a.m.
NED2 Entity disambiguation (via description) batch_6a16445dadc0819097f49aaab619afc8 completed May 27, 2026, 1:09 a.m.
Created at: April 28, 2026, 12:43 a.m.