Triple

T29962737
Position Surface form Disambiguated ID Type / Status
Subject Angeles City E761094 entity
Predicate hasUniversity P113 FINISHED
Object Angeles University Foundation
Angeles University Foundation is a private Catholic higher education institution in Angeles City, Philippines, known for its medical, allied health, and professional programs.
E1893841 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: Angeles University Foundation | Statement: [Angeles City, hasUniversity, Angeles University Foundation]
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: Angeles University Foundation
Triple: [Angeles City, hasUniversity, Angeles University Foundation]
Generated description
Angeles University Foundation is a private Catholic higher education institution in Angeles City, Philippines, known for its medical, allied health, and professional programs.

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_69f22466327481908ba6db916837bece completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67866e9248190b7ba218f9ca2ae8d completed May 2, 2026, 10:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2721f1d86c8190b285e4a1225ac7c2 completed June 8, 2026, 8:11 p.m.
NEDg Description generation batch_6a272333f384819084456b384bc17a6c completed June 8, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a2723e303108190a1e6d1965b21a8e8 completed June 8, 2026, 8:19 p.m.
Created at: April 29, 2026, 6:29 p.m.