Triple

T35224871
Position Surface form Disambiguated ID Type / Status
Subject Hilario G. Davide Jr. E1017062 entity
Predicate educatedAt P5 FINISHED
Object University of the Visayas
The University of the Visayas is a private higher education institution in Cebu, Philippines, known for its diverse academic programs and role in producing prominent Filipino professionals and public servants.
E2172781 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: University of the Visayas | Statement: [Hilario G. Davide Jr., educatedAt, University of the Visayas]
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: University of the Visayas
Triple: [Hilario G. Davide Jr., educatedAt, University of the Visayas]
Generated description
The University of the Visayas is a private higher education institution in Cebu, Philippines, known for its diverse academic programs and role in producing prominent Filipino professionals and public servants.

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_69f76de072908190ab65038a8a7b6a79 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78ea6cf5881909be769dec26ec262 completed May 3, 2026, 6:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3933f3916c8190a32477213432cbd9 completed June 22, 2026, 1:09 p.m.
NEDg Description generation batch_6a39350a315c8190838fa2987f631da1 completed June 22, 2026, 1:13 p.m.
NED2 Entity disambiguation (via description) batch_6a39356e210c8190badece11b58c96b2 completed June 22, 2026, 1:15 p.m.
Created at: May 3, 2026, 4:02 p.m.