Triple

T37345390
Position Surface form Disambiguated ID Type / Status
Subject Gifu University E927157 entity
Predicate hasCampus P116 FINISHED
Object Gifu University Hospital campus
Gifu University Hospital campus is the medical campus of Gifu University that houses its affiliated teaching hospital and related healthcare and research facilities.
 E2224201 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: Gifu University Hospital campus | Statement: [Gifu University, hasCampus, Gifu University Hospital campus]
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: Gifu University Hospital campus
Triple: [Gifu University, hasCampus, Gifu University Hospital campus]
Generated description
Gifu University Hospital campus is the medical campus of Gifu University that houses its affiliated teaching hospital and related healthcare and research facilities.

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_69f76eb5e034819088e53ab5b7909a68 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5b99e6dc819099aff56677d41a65 completed May 6, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a406cdafc048190a136b0d31b32d37a completed June 28, 2026, 12:37 a.m.
NEDg Description generation batch_6a406e1111c08190af357e4e318772ba completed June 28, 2026, 12:42 a.m.
NED2 Entity disambiguation (via description) batch_6a406ed77a5c819091554d7e4561aa0b completed June 28, 2026, 12:46 a.m.
Created at: May 3, 2026, 4:16 p.m.