Triple

T33242661
Position Surface form Disambiguated ID Type / Status
Subject Toucheng Township E851007 entity
Predicate hasAttraction P105 FINISHED
Object Toucheng Old Street
Toucheng Old Street is a historic commercial street in Yilan County, Taiwan, known for its preserved traditional architecture, old shophouses, and local snacks that reflect the town’s early development as a coastal trading hub.
 E2040887 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: Toucheng Old Street | Statement: [Toucheng Township, hasAttraction, Toucheng Old Street]
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: Toucheng Old Street
Triple: [Toucheng Township, hasAttraction, Toucheng Old Street]
Generated description
Toucheng Old Street is a historic commercial street in Yilan County, Taiwan, known for its preserved traditional architecture, old shophouses, and local snacks that reflect the town’s early development as a coastal trading hub.

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_69f34962386c81909ddc3bf9e18ddeb8 completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6daf0cd708190ab344d594aad93cc completed May 3, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a352fe205c4819092dd2217651a01f8 completed June 19, 2026, 12:02 p.m.
NEDg Description generation batch_6a35307919d8819084df460c1aa040f7 completed June 19, 2026, 12:05 p.m.
NED2 Entity disambiguation (via description) batch_6a3530fc1f488190a8070ae5223fc28f completed June 19, 2026, 12:07 p.m.
Created at: May 1, 2026, 1:31 a.m.