Triple
T1463740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philip G. Saffman |
E31570
|
entity |
| Predicate | inAcademicField |
P934
|
FINISHED |
| Object | mechanics |
—
|
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: mechanics | Statement: [Philip G. Saffman, inAcademicField, mechanics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inAcademicField Context triple: [Philip G. Saffman, inAcademicField, mechanics]
-
A.
academicFocus
Indicates the primary field of study, discipline, or subject area that an entity concentrates on academically.
-
B.
usesResearchSubject
Indicates that one entity employs or utilizes another entity as a research subject in a study or investigation.
-
C.
academicType
Indicates the specific academic category or classification associated with an entity (such as a work, program, or role).
-
D.
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.
-
E.
academicBody
Indicates a formal organizational relationship in which an entity functions as an academic institution or governing academic unit associated with another entity.
- 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_69a49917dfc081909acdbdf5d684f1ef |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c5b89708819084fb9ba4ff293b8b |
completed | March 1, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69a4c48121e48190946c23c583e5fb64 |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8 p.m.