Triple
T12422268
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Utica University |
E296803
|
entity |
| Predicate | hasCybersecurityReputation |
P40353
|
FINISHED |
| Object | known for cybersecurity programs |
—
|
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: known for cybersecurity programs | Statement: [Utica University, hasCybersecurityReputation, known for cybersecurity programs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCybersecurityReputation Context triple: [Utica University, hasCybersecurityReputation, known for cybersecurity programs]
-
A.
securityReputation
chosen
Indicates the assessed trustworthiness or risk level associated with an entity’s security posture or behavior.
-
B.
defensiveReputation
Indicates that an entity is regarded or recognized as being strong, reliable, or skilled in defense.
-
C.
haveReputation
Indicates that an entity is recognized or regarded in a certain way by others, reflecting its perceived character, quality, or status.
-
D.
engineeringReputation
Indicates the perceived quality, credibility, or esteem of an entity’s engineering capabilities or output as judged by others.
-
E.
hasPolicyReputationFor
Indicates that an entity is recognized or regarded in a particular way with respect to its policies or policy-related behavior.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94e1888b48190bd750f839a26e99e |
completed | April 10, 2026, 7:23 p.m. |
| PD | Predicate disambiguation | batch_69d94d354b488190adc83fb4f2770dd5 |
completed | April 10, 2026, 7:19 p.m. |
Created at: April 8, 2026, 9:55 p.m.