Triple
T10861702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rothari |
E256419
|
entity |
| Predicate | lawCodeScope |
P25835
|
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: [Rothari, lawCodeScope, civil law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lawCodeScope Context triple: [Rothari, lawCodeScope, civil law]
-
A.
legalScope
chosen
Indicates the range, boundaries, or extent of authority, applicability, or effect that something has under a particular legal framework or rule.
-
B.
legalCodeAppliesTo
Indicates that a particular legal code or statute is applicable to, or governs, a specified subject, situation, or entity.
-
C.
legalCodeName
Indicates that one entity is the official legal code designation or name assigned to another entity within a legal or regulatory system.
-
D.
legalCodeScript
Indicates that a legal code is written or represented using a particular writing system or script.
-
E.
legalCodeFocus
Indicates that something is specifically concerned with, centered on, or primarily addressing a particular legal code or body of law.
- 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_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7515186f08190a5cc388a7d936c4f |
completed | April 9, 2026, 7:12 a.m. |
| PD | Predicate disambiguation | batch_69d70d308dfc81908792f98cfb871392 |
completed | April 9, 2026, 2:21 a.m. |
Created at: April 8, 2026, 9:20 p.m.