Triple
T34186771
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of Skövde |
E876982
|
entity |
| Predicate | hasFaculty |
P141
|
FINISHED |
| Object |
School of Engineering Science
The School of Engineering Science is an academic division of the University of Skövde specializing in engineering and applied science education and research.
|
E2085573
|
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 Engineering Science | Statement: [University of Skövde, hasFaculty, School of Engineering Science]
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 Engineering Science Triple: [University of Skövde, hasFaculty, School of Engineering Science]
Generated description
The School of Engineering Science is an academic division of the University of Skövde specializing in engineering and applied science education and research.
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_69f349ae640c8190b9cd220b5368d8b6 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7100a7898819092ba06f35251fc7c |
completed | May 3, 2026, 9:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a36cc7e6d788190908da962a63abcd8 |
completed | June 20, 2026, 5:23 p.m. |
| NEDg | Description generation | batch_6a36cd2dca188190b21b8e2a18af2b87 |
completed | June 20, 2026, 5:26 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a36cdb7e3c48190982ef46371260e77 |
completed | June 20, 2026, 5:28 p.m. |
Created at: May 1, 2026, 1:55 a.m.