Triple
T4021162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elsevier |
E91281
|
entity |
| Predicate | operates |
P24
|
FINISHED |
| Object | Scopus |
E16219
|
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: Scopus | Statement: [Elsevier, operates, Scopus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scopus Context triple: [Elsevier, operates, Scopus]
-
A.
Scopus
chosen
Scopus is a large abstract and citation database of peer-reviewed literature covering scientific, technical, medical, and social science research.
-
B.
Web of Science
Web of Science is a major multidisciplinary citation indexing and abstracting database widely used for academic research and bibliometric analysis.
-
C.
Ei Compendex
Ei Compendex is a comprehensive engineering literature database that indexes scientific and technical research publications across a wide range of engineering disciplines.
-
D.
Clarivate Analytics
Clarivate Analytics is a global analytics company specializing in providing research, citation, patent, and intellectual property data and tools for academia, corporations, and governments.
-
E.
ScienceDirect
ScienceDirect is a leading full-text scientific database providing access to a large collection of peer-reviewed journals and books across numerous disciplines.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69aed9618b04819081750d979d2af098 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefacb4c208190b8dd595a534850b2 |
completed | March 9, 2026, 4:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b54c7fd474819097766194ca8d165d |
completed | March 14, 2026, 11:54 a.m. |
Created at: March 9, 2026, 3:35 p.m.