Triple

T30088825
Position Surface form Disambiguated ID Type / Status
Subject Rize Valley E764671 entity
Predicate hasAttraction P105 FINISHED
Object Grass Lake
Grass Lake is a scenic, turquoise-colored alpine lake in Jiuzhaigou’s Rize Valley in Sichuan, China, known for its clear waters and surrounding forested mountain landscape.
 E2088846 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: Grass Lake | Statement: [Rize Valley, hasAttraction, Grass Lake]
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: Grass Lake
Triple: [Rize Valley, hasAttraction, Grass Lake]
Generated description
Grass Lake is a scenic, turquoise-colored alpine lake in Jiuzhaigou’s Rize Valley in Sichuan, China, known for its clear waters and surrounding forested mountain landscape.

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_69f22473c0fc8190a926a8051b3b378b completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67d6e9e188190808014372fc2cbd0 completed May 2, 2026, 10:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a36e5fbcdb0819099c20a337a9c0f99 completed June 20, 2026, 7:11 p.m.
NEDg Description generation batch_6a36e90d94788190b528a81f3cafe3b3 completed June 20, 2026, 7:25 p.m.
NED2 Entity disambiguation (via description) batch_6a36e9736fc48190990a081dc29457f5 completed June 20, 2026, 7:26 p.m.
Created at: April 29, 2026, 7:05 p.m.