Triple
T23745227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eckart Viehweg |
E586795
|
entity |
| Predicate | mainResearchInterest |
P934
|
FINISHED |
| Object | moduli of higher-dimensional varieties |
—
|
LITERAL 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: moduli of higher-dimensional varieties | Statement: [Eckart Viehweg, mainResearchInterest, moduli of higher-dimensional varieties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainResearchInterest Context triple: [Eckart Viehweg, mainResearchInterest, moduli of higher-dimensional varieties]
-
A.
usesResearchSubject
Indicates that one entity employs or utilizes another entity as a research subject in a study or investigation.
-
B.
researchTopic
Indicates that a subject conducts or focuses research on a particular topic or area of study.
-
C.
regionOfAcademicInterest
Indicates that an entity has a particular academic field or subject area as its focus of interest or study.
-
D.
isInResearchArea
Indicates that one entity falls within, or is relevant to, the specified research area of another entity.
-
E.
hasResearchArea
chosen
Indicates that an entity (such as a person, project, or organization) is associated with or focused on a particular field or area of research.
- F. None of above.
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_69e24908efb08190bf755c3a9b91f222 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1bcbcc4f88190a00fceeafbfb5cfd |
completed | April 29, 2026, 8:09 a.m. |
| PD | Predicate disambiguation | batch_69f155f012808190a4b1cbc155558ade |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 7:12 p.m.