Triple
T8746680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evinayong |
E207845
|
entity |
| Predicate | legalSystemOfCountry |
P81768
|
FINISHED |
| Object | civil 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: civil law | Statement: [Evinayong, legalSystemOfCountry, civil law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalSystemOfCountry Context triple: [Evinayong, legalSystemOfCountry, civil law]
-
A.
legalSystem
Indicates the formal framework of laws, rules, and institutions that governs how legal matters are defined, interpreted, and enforced within a society or jurisdiction.
-
B.
countryOfLegalSystem
chosen
Indicates the relationship between a legal system and the country in which that legal system is officially established or applied.
-
C.
relatedLegalSystem
Indicates that there is an association or connection between two legal systems, such as influence, similarity, shared origin, or mutual relevance.
-
D.
legalSystemDepictedAs
Indicates that one entity portrays, represents, or characterizes a legal system in a particular way or form.
-
E.
legalSystemWorkedIn
Indicates that a person carried out their professional legal activities within a particular legal system or jurisdiction.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d75e7c88190a9e78fadb979e1b6 |
completed | March 31, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69cc5c160dac8190b4aeb4bf0529de52 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:39 p.m.