Triple

T31849033
Position Surface form Disambiguated ID Type / Status
Subject Hainan Hlai E813015 entity
Predicate hasDialects P4251 FINISHED
Object Baisha Hlai
Baisha Hlai is a major dialect of the Hlai language spoken by the Li ethnic group in Baisha County on China’s Hainan Island.
E1981728 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: Baisha Hlai | Statement: [Hainan Hlai, hasDialects, Baisha Hlai]
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: Baisha Hlai
Triple: [Hainan Hlai, hasDialects, Baisha Hlai]
Generated description
Baisha Hlai is a major dialect of the Hlai language spoken by the Li ethnic group in Baisha County on China’s Hainan Island.

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_69f348eb327881909b4584b925742f6e completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6b03aa0d88190bf436695207e35c7 completed May 3, 2026, 2:17 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e7fd5a1548190a12f4262abab863a completed June 14, 2026, 10:17 a.m.
NEDg Description generation batch_6a2e808c5b1081909fdf8a3c7ab91459 completed June 14, 2026, 10:21 a.m.
NED2 Entity disambiguation (via description) batch_6a2e8155e4788190badcebd73ae27e02 completed June 14, 2026, 10:24 a.m.
Created at: April 30, 2026, 11:51 p.m.