Triple
T23307369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baduy people |
E590486
|
entity |
| Predicate | customaryLawName |
P9797
|
FINISHED |
| Object | pikukuh |
—
|
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: pikukuh | Statement: [Baduy people, customaryLawName, pikukuh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: customaryLawName Context triple: [Baduy people, customaryLawName, pikukuh]
-
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.
usedLegalSystemOf
Indicates that one entity applied, followed, or operated under the legal system or body of laws belonging to another entity.
-
C.
civilLawBasedOn
Indicates that a civil law, legal system, or legal provision is derived from, grounded in, or significantly influenced by another specified source of law or legal framework.
-
D.
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.
-
E.
haveCivilLaw
Indicates that an entity is subject to, governed by, or operates under a civil law legal system.
- 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_69e25d1c0ecc8190a355aa229f06d0e0 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1972846fc819092ca2b9590b2e177 |
completed | April 29, 2026, 5:29 a.m. |
| PD | Predicate disambiguation | batch_69effcf325f88190b320268c3c551abb |
completed | April 28, 2026, 12:18 a.m. |
Created at: April 17, 2026, 5:05 p.m.