Triple

T31220971
Position Surface form Disambiguated ID Type / Status
Subject Liaoning University E796007 entity
Predicate hasFaculty P141 FINISHED
Object School of International Relations
The School of International Relations is an academic unit of Liaoning University specializing in the study and research of global politics, diplomacy, and international affairs.
 E1951563 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: School of International Relations | Statement: [Liaoning University, hasFaculty, School of International Relations]
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: School of International Relations
Triple: [Liaoning University, hasFaculty, School of International Relations]
Generated description
The School of International Relations is an academic unit of Liaoning University specializing in the study and research of global politics, diplomacy, and international affairs.

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_69f224d9d52c8190a61f68ded37fa755 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69c4d35848190bd5a2dd81851bea3 completed May 3, 2026, 12:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29592f0ffc81908f4513f97c21c52c completed June 10, 2026, 12:31 p.m.
NEDg Description generation batch_6a295d2ada288190aaf4ba844b770666 completed June 10, 2026, 12:48 p.m.
NED2 Entity disambiguation (via description) batch_6a295e13ebbc8190bba5d5efe052b6ea completed June 10, 2026, 12:52 p.m.
Created at: April 29, 2026, 9:10 p.m.