Triple

T28160932
Position Surface form Disambiguated ID Type / Status
Subject Xichang E714889 entity
Predicate touristAttraction P530 FINISHED
Object Qionghai Lake scenic area
Qionghai Lake scenic area is a popular lakeside tourist destination in Xichang, Sichuan, known for its tranquil waters, surrounding mountains, and recreational activities.
E1805827 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: Qionghai Lake scenic area | Statement: [Xichang, touristAttraction, Qionghai Lake scenic area]
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: Qionghai Lake scenic area
Triple: [Xichang, touristAttraction, Qionghai Lake scenic area]
Generated description
Qionghai Lake scenic area is a popular lakeside tourist destination in Xichang, Sichuan, known for its tranquil waters, surrounding mountains, and recreational activities.

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_69efd6b156448190bfa15958208395c3 completed April 27, 2026, 9:35 p.m.
NER Named-entity recognition batch_69f641eb9eec8190a50b58f0f28983b8 completed May 2, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15d7b5948881909f0e4c94aeacc829 completed May 26, 2026, 5:26 p.m.
NEDg Description generation batch_6a15d843d2408190916068c6aa442552 completed May 26, 2026, 5:28 p.m.
NED2 Entity disambiguation (via description) batch_6a15d8fcf91c8190b1e6026a44cf66a2 completed May 26, 2026, 5:31 p.m.
Created at: April 27, 2026, 10:06 p.m.