Triple
T28641799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Clara University School of Law |
E724943
|
entity |
| Predicate | programSpecialty |
P13679
|
FINISHED |
| Object | intellectual property law |
—
|
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: intellectual property law | Statement: [Santa Clara University School of Law, programSpecialty, intellectual property law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: programSpecialty Context triple: [Santa Clara University School of Law, programSpecialty, intellectual property law]
-
A.
subjectSpecialization
Indicates that one subject focuses on, or has expertise in, a particular field, topic, or area of knowledge.
-
B.
institutionSpecialization
Indicates that an institution focuses on, is dedicated to, or has expertise in a particular field, domain, or area of activity.
-
C.
programDesignation
Indicates that an entity is assigned or identified by a specific program designation or label.
-
D.
programFocus
chosen
Indicates that an educational or training program is primarily oriented around or concentrated on a particular subject, theme, or objective.
-
E.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
- 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_69f01d8423888190bd2f4e52605bf261 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69f68805b4848190b75da14996d52a38 |
completed | May 2, 2026, 11:25 p.m. |
| PD | Predicate disambiguation | batch_69f68609c0b08190a8e1238a4d97c270 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 28, 2026, 4:45 a.m.