Triple
T13318894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Publons |
E317263
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Publons |
E317263
|
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: Publons | Statement: [Publons, name, Publons]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Publons Context triple: [Publons, name, Publons]
-
A.
Publons
chosen
Publons is an online platform that helps researchers track, verify, and showcase their peer review and editorial contributions for academic journals.
-
B.
Crossref
Crossref is a nonprofit organization that provides persistent digital identifiers and metadata services for scholarly content to support reliable citation linking and research discovery.
-
C.
Semantic Scholar
Semantic Scholar is an AI-powered academic search engine that helps researchers discover and understand scientific literature more efficiently.
-
D.
SciVal
SciVal is an Elsevier analytics platform that provides research performance metrics and benchmarking tools for institutions, researchers, and policymakers.
-
E.
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.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d990faa95481908a7fd297959c062e |
completed | April 11, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716ee695c81909ffeeb0901ee66c1 |
completed | May 3, 2026, 9:35 a.m. |
Created at: April 9, 2026, 9:29 p.m.