Triple
T36036955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kandyan law |
E1042429
|
entity |
| Predicate | appliesToMatter |
P1129
|
FINISHED |
| Object | non-consanguineous marriage |
—
|
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: non-consanguineous marriage | Statement: [Kandyan law, appliesToMatter, non-consanguineous marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliesToMatter Context triple: [Kandyan law, appliesToMatter, non-consanguineous marriage]
-
A.
appliesTo
chosen
Indicates that something is relevant, valid, or has effect in relation to a particular entity, case, or context.
-
B.
appliesToFieldOfLaw
Indicates that something is relevant or applicable to a particular field or branch of law.
-
C.
MatterSupports
Indicates that one physical substance or object provides structural support or a stable base for another.
-
D.
appliesPrimarilyTo
Indicates that a property, rule, or characteristic is mainly relevant or intended for a particular entity or group, more than for others.
-
E.
appliesAlsoTo
Indicates that a condition, rule, or characteristic that applies to one entity is additionally applicable to 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_69f76e2d7e8c8190bac4e90734566799 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69ff5f5ecc808190b2df364da108ff4c |
completed | May 9, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69ff5b84131c8190bf81d7fb53e934bc |
completed | May 9, 2026, 4:06 p.m. |
Created at: May 3, 2026, 4:07 p.m.