Triple
T22177981
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nature Materials |
E548095
|
entity |
| Predicate | indexingService |
P6000
|
FINISHED |
| Object | Scopus |
—
|
NE NERFINISHED |
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: [Nature Materials, indexingService, Scopus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scopus Context triple: [Nature Materials, indexingService, 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.
SCImago Research Group
SCImago Research Group is an academic research organization best known for creating bibliometric indicators and journal rankings that analyze and visualize scientific output and impact worldwide.
-
E.
SciVal
SciVal is an Elsevier analytics platform that provides research performance metrics and benchmarking tools for institutions, researchers, and policymakers.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e3d53f88190a2b690e3f25bb062 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a6dd5a081908035e81c068d8d5a |
completed | April 28, 2026, 9:45 p.m. |
Created at: April 16, 2026, 8:34 p.m.