Triple

T37219686
Position Surface form Disambiguated ID Type / Status
Subject Hotel Granvia E922842 entity
Predicate hasHotel P4287 FINISHED
Object Hotel Granvia Hiroshima
Hotel Granvia Hiroshima is a modern, upscale hotel directly connected to Hiroshima Station, popular with both business and leisure travelers for its convenient location and city views.
 E2218048 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: Hotel Granvia Hiroshima | Statement: [Hotel Granvia, hasHotel, Hotel Granvia Hiroshima]
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: Hotel Granvia Hiroshima
Triple: [Hotel Granvia, hasHotel, Hotel Granvia Hiroshima]
Generated description
Hotel Granvia Hiroshima is a modern, upscale hotel directly connected to Hiroshima Station, popular with both business and leisure travelers for its convenient location and city 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_69f76ea6f5288190b8d9988f613811c0 completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb369ca260819088b651347542cb83 completed May 6, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40362b4ad8819086b13d76cddb4c6c completed June 27, 2026, 8:44 p.m.
NEDg Description generation batch_6a4036cfd8dc8190ad624df103d52eb7 completed June 27, 2026, 8:47 p.m.
NED2 Entity disambiguation (via description) batch_6a40392916908190867f36144e479af5 completed June 27, 2026, 8:57 p.m.
Created at: May 3, 2026, 4:15 p.m.