Triple

T25335728
Position Surface form Disambiguated ID Type / Status
Subject Universitas Negeri Malang E635272 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Psychology
The Faculty of Psychology is an academic division of Universitas Negeri Malang dedicated to education and research in psychological science and its applications.
E1675968 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: Faculty of Psychology | Statement: [Universitas Negeri Malang, hasFaculty, Faculty of Psychology]
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: Faculty of Psychology
Triple: [Universitas Negeri Malang, hasFaculty, Faculty of Psychology]
Generated description
The Faculty of Psychology is an academic division of Universitas Negeri Malang dedicated to education and research in psychological science and its applications.

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_69e75a99bd6481909476115b35b9a8e4 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f497ca0f18819090168e3221c2aa32 completed May 1, 2026, 12:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1075f2f78c8190a53a12931482cc8a completed May 22, 2026, 3:27 p.m.
NEDg Description generation batch_6a10771a5a648190844a509e6ac507be completed May 22, 2026, 3:32 p.m.
NED2 Entity disambiguation (via description) batch_6a1077d01fa08190b5439eba879538ef completed May 22, 2026, 3:35 p.m.
Created at: April 21, 2026, 1:32 p.m.