Triple
T9065869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | People's Courts (AANES) |
E217243
|
entity |
| Predicate | recognizesCustomaryLaw |
P9797
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [People's Courts (AANES), recognizesCustomaryLaw, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: recognizesCustomaryLaw Context triple: [People's Courts (AANES), recognizesCustomaryLaw, yes]
-
A.
customaryLaw
chosen
Indicates that a relationship, behavior, or rule is governed by unwritten, traditional norms and practices recognized as binding within a community or group.
-
B.
haveCivilLaw
Indicates that an entity is subject to, governed by, or operates under a civil law legal system.
-
C.
relatedLegalSystem
Indicates that there is an association or connection between two legal systems, such as influence, similarity, shared origin, or mutual relevance.
-
D.
separateLegalSystem
Indicates that one entity maintains its own distinct and independent legal system from another entity.
-
E.
coexistingLegalTradition
Indicates that multiple legal traditions or systems exist and operate simultaneously within the same social or institutional context.
- 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_69ca83d5a7f48190b16c1e59bd43ede0 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc94bc91d48190acf62afa90eec079 |
completed | April 1, 2026, 3:45 a.m. |
| PD | Predicate disambiguation | batch_69cc65f881248190bfd220bb28a9fb5f |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:11 p.m.