Triple
T33540783
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Severinus de Monzambano Veronensis |
E859063
|
entity |
| Predicate | bearerMainInterest |
P44657
|
FINISHED |
| Object | natural 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: natural law | Statement: [Severinus de Monzambano Veronensis, bearerMainInterest, natural law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bearerMainInterest Context triple: [Severinus de Monzambano Veronensis, bearerMainInterest, natural law]
-
A.
subjectInterest
Indicates that the subject has an interest in, or is concerned with, the object.
-
B.
recognizedInterest
Indicates that one entity has formally acknowledged or identified another entity’s interest in something as valid or relevant.
-
C.
interestMayBe
Indicates that one entity potentially has an interest in, or may be interested in, another entity or subject.
-
D.
primaryInterest
chosen
Indicates that one entity is the main or most significant focus of attention, concern, or engagement for another entity.
-
E.
secondaryInterest
Indicates that an entity has a secondary, less primary but still relevant interest or focus in relation to another entity or topic.
- 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_69f3497a5be08190a39b12736899e034 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f7431c0eec81909ead443e07d75e18 |
completed | May 3, 2026, 12:44 p.m. |
| PD | Predicate disambiguation | batch_69f74143cf708190a12d487884298437 |
completed | May 3, 2026, 12:36 p.m. |
Created at: May 1, 2026, 1:39 a.m.