Triple
T22868117
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gouvernement général de l’Algérie |
E567111
|
entity |
| Predicate | legalSystemImposed |
P150039
|
FINISHED |
| Object | droit français |
—
|
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: droit français | Statement: [Gouvernement général de l’Algérie, legalSystemImposed, droit français]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalSystemImposed Context triple: [Gouvernement général de l’Algérie, legalSystemImposed, droit français]
-
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.
legalSystemDepictedAs
Indicates that one entity portrays, represents, or characterizes a legal system in a particular way or form.
-
C.
legalSystemFeature
Indicates a characteristic, rule, or structural element that forms part of a particular legal system.
-
D.
legalSystemWorkedIn
Indicates that a person carried out their professional legal activities within a particular legal system or jurisdiction.
-
E.
enforcedLaw
Indicates that an authority actively applies or upholds a specific law to regulate behavior or resolve situations.
- F. None of above. chosen
Provenance (4 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_69e24589083081908d5694c4fdc80086 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f02c8b8819095cbee626f935fed |
completed | April 29, 2026, 3:46 a.m. |
| PD | Predicate disambiguation | batch_69eed2d8c0608190afef4c4e530c0e2c |
completed | April 27, 2026, 3:07 a.m. |
| PDg | Predicate description generation | batch_69eeeb577e2081909f4a4e9c296535c0 |
completed | April 27, 2026, 4:51 a.m. |
Created at: April 17, 2026, 3:38 p.m.