Triple
T482190
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jane C. Ginsburg |
E9192
|
entity |
| Predicate | areaOfSpecialization |
P3
|
FINISHED |
| Object | international copyright |
—
|
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: international copyright | Statement: [Jane C. Ginsburg, areaOfSpecialization, international copyright]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: areaOfSpecialization Context triple: [Jane C. Ginsburg, areaOfSpecialization, international copyright]
-
A.
hasSpecialty
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
subDisciplineOf
Indicates that one discipline is a more specialized or narrower field within another, broader discipline.
-
C.
hasResearchArea
Indicates that an entity (such as a person, project, or organization) is associated with or focused on a particular field or area of research.
-
D.
fieldOfWork
chosen
Indicates the professional or academic domain in which an entity is primarily engaged or specializes.
-
E.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f05a7f6c819082b4a5a3e69468a6 |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf321288190b5d560f75782c2cb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.