Triple

T36406422
Position Surface form Disambiguated ID Type / Status
Subject Lin Dan E896759 entity
Predicate placeOfBirth P1 FINISHED
Object Longyan, Fujian, China
Longyan, in Fujian Province, China, is a mountainous prefecture-level city known for its Hakka culture, tulou earthen buildings, and as the hometown of badminton legend Lin Dan.
E2181336 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: Longyan, Fujian, China | Statement: [Lin Dan, placeOfBirth, Longyan, Fujian, China]
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: Longyan, Fujian, China
Triple: [Lin Dan, placeOfBirth, Longyan, Fujian, China]
Generated description
Longyan, in Fujian Province, China, is a mountainous prefecture-level city known for its Hakka culture, tulou earthen buildings, and as the hometown of badminton legend Lin Dan.

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_69f76e53b81081908d3b81860593f38a completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7bd1926e88190b367702da4f65270 completed May 3, 2026, 9:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39b446da2c819084561b0a0118e41d completed June 22, 2026, 10:16 p.m.
NEDg Description generation batch_6a39b606ff6c8190bab885ad364692c6 completed June 22, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_6a39b6d4ad4c8190b9f79b26b380f446 completed June 22, 2026, 10:27 p.m.
Created at: May 3, 2026, 4:10 p.m.