Triple
T6750116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doctor of Laws (Dr. iur.) |
E154320
|
entity |
| Predicate | commonSpecializations |
P36338
|
FINISHED |
| Object | public 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: public law | Statement: [Doctor of Laws (Dr. iur.), commonSpecializations, public law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: commonSpecializations Context triple: [Doctor of Laws (Dr. iur.), commonSpecializations, public law]
-
A.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
B.
uniformSpecialty
chosen
Indicates that multiple entities share the same specific specialty, expertise, or area of focus.
-
C.
regionSpecialization
Indicates that a region is designated or recognized as being particularly focused on, adapted to, or specialized in a specific function, activity, or domain.
-
D.
hasSpecialist
Indicates that one entity is associated with or assigned to a specialist entity that provides expert support, service, or oversight for it.
-
E.
marketSpecialization
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
- 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_69c6880ef37881909268a5a7299b9293 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d327e37081909d576e6eff9eec97 |
completed | March 27, 2026, 6:57 p.m. |
| PD | Predicate disambiguation | batch_69c6d09227108190b253b91967831a85 |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:11 p.m.