Triple

T26816037
Position Surface form Disambiguated ID Type / Status
Subject Tai O E675122 entity
Predicate hasStructure P35 FINISHED
Object Tai O Heritage Hotel
Tai O Heritage Hotel is a boutique hotel housed in a restored colonial-era police station in Hong Kong’s Tai O fishing village, known for its blend of heritage conservation and scenic coastal views.
E1741920 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: Tai O Heritage Hotel | Statement: [Tai O, hasStructure, Tai O Heritage Hotel]
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: Tai O Heritage Hotel
Triple: [Tai O, hasStructure, Tai O Heritage Hotel]
Generated description
Tai O Heritage Hotel is a boutique hotel housed in a restored colonial-era police station in Hong Kong’s Tai O fishing village, known for its blend of heritage conservation and scenic coastal views.

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_69eee9b6b28481909332f83eb17e5170 completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61a85eee481909550d515a5b8feea completed May 2, 2026, 3:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12097909c08190ad68858afa30a187 completed May 23, 2026, 8:09 p.m.
NEDg Description generation batch_6a120a8e2edc8190891be0f695a8c0e1 completed May 23, 2026, 8:14 p.m.
NED2 Entity disambiguation (via description) batch_6a120b9ce700819089a799bf42cbbac9 completed May 23, 2026, 8:18 p.m.
Created at: April 27, 2026, 4:52 a.m.