Triple
T19857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John H. Sununu |
E395
|
entity |
| Predicate | degreeEarned |
P6
|
FINISHED |
| Object | PhD in mechanical engineering |
—
|
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: PhD in mechanical engineering | Statement: [John H. Sununu, degreeEarned, PhD in mechanical engineering]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: degreeEarned Context triple: [John H. Sununu, degreeEarned, PhD in mechanical engineering]
-
A.
academicDegree
chosen
Indicates that an entity holds or has been awarded a specific academic degree.
-
B.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
C.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
D.
offersDegree
Indicates that an institution or program provides a specific academic degree as an available qualification.
-
E.
isStudiedIn
Indicates that a subject (such as a topic, field, or phenomenon) is examined, researched, or learned about within a particular context, environment, or discipline.
- 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_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a24703cb988190ad2bc181d27829e4 |
completed | Feb. 28, 2026, 1:38 a.m. |
| PD | Predicate disambiguation | batch_69a24650f1f0819081e638fafd18d687 |
completed | Feb. 28, 2026, 1:35 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.