Triple

T32323999
Position Surface form Disambiguated ID Type / Status
Subject Universiti Malaysia Pahang E825853 entity
Predicate hasFaculty P141 FINISHED
Object Centre for Human Sciences
The Centre for Human Sciences is an academic unit at Universiti Malaysia Pahang that focuses on teaching and research in the humanities and social sciences to complement the university’s technical and engineering disciplines.
E2002440 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: Centre for Human Sciences | Statement: [Universiti Malaysia Pahang, hasFaculty, Centre for Human Sciences]
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: Centre for Human Sciences
Triple: [Universiti Malaysia Pahang, hasFaculty, Centre for Human Sciences]
Generated description
The Centre for Human Sciences is an academic unit at Universiti Malaysia Pahang that focuses on teaching and research in the humanities and social sciences to complement the university’s technical and engineering disciplines.

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_69f34912d0c48190bba75770660320e9 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bde5bd1c8190b6dabd5ebefd8947 completed May 3, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a305720317c8190b3eae01c963c92ff completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a31b5cd810c8190b3166a3348c6c041 completed June 16, 2026, 8:45 p.m.
NED2 Entity disambiguation (via description) batch_6a31b67e68a081908ebaf60ca5539865 completed June 16, 2026, 8:47 p.m.
Created at: May 1, 2026, 12:47 a.m.