Triple
T631262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugenio Montero Ríos |
E15930
|
entity |
| Predicate | legalProfession |
P5610
|
FINISHED |
| Object | professor of 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: professor of law | Statement: [Eugenio Montero Ríos, legalProfession, professor of law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalProfession Context triple: [Eugenio Montero Ríos, legalProfession, professor of law]
-
A.
legalProfessionRole
chosen
Indicates that one entity holds or performs a specific professional role within the legal domain in relation to another entity or context.
-
B.
legalRepresentation
Indicates that one entity formally acts on behalf of another in legal matters, such as providing counsel, advocacy, or defense within a legal system.
-
C.
practicedLawIn
Indicates that a person engaged in the professional practice of law within a specified jurisdiction or location.
-
D.
branchOfLaw
Indicates a relationship where one legal field or discipline is a subdivision or specialized area within a broader body of law.
-
E.
legalTraining
Indicates that one entity has provided or received education or instruction in law from another entity.
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49ec171008190ab91dee86e9279af |
completed | March 1, 2026, 8:17 p.m. |
| PD | Predicate disambiguation | batch_69a49d030c648190ba1a02301b45f694 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.