Triple
T1489448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dedman School of Law |
E29542
|
entity |
| Predicate | hasProfessionalSchoolType |
P19184
|
FINISHED |
| Object | law school |
—
|
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: law school | Statement: [Dedman School of Law, hasProfessionalSchoolType, law school]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalSchoolType Context triple: [Dedman School of Law, hasProfessionalSchoolType, law school]
-
A.
hasDoctoralSchool
Indicates that an individual or academic entity is affiliated with or obtained their doctoral education from a specific doctoral school or graduate institution.
-
B.
hasMedicalCollege
Indicates that one entity possesses, hosts, or includes a medical college as part of its organization or structure.
-
C.
hasSchoolCategory
chosen
Indicates that an entity (such as a school or educational institution) is associated with a particular category or type of school.
-
D.
hasSchool
Indicates that an entity possesses, is associated with, or is served by a particular school.
-
E.
hasEducationalProgram
Indicates that an entity offers, runs, or is associated with a specific educational program.
- 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_69a498da82e08190ba833330d05f380f |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6a6095481909e9d406ac9a41828 |
completed | March 1, 2026, 11:07 p.m. |
| PD | Predicate disambiguation | batch_69a4c48902808190a8028d359bcf123e |
completed | March 1, 2026, 10:58 p.m. |
Created at: March 1, 2026, 8:12 p.m.