Triple
T2148499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bantia |
E47124
|
entity |
| Predicate | legalText |
P12392
|
FINISHED |
| Object | municipal law code |
—
|
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: municipal law code | Statement: [Bantia, legalText, municipal law code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalText Context triple: [Bantia, legalText, municipal law code]
-
A.
legalDetail
Indicates that there is specific legal information, conditions, or attributes associated with the related entity or relationship.
-
B.
legalContext
Indicates that the relationship or action occurs within, is shaped by, or is relevant to a specific legal framework, proceeding, or set of legal norms.
-
C.
legalDocumentRelatedToRights
chosen
Indicates a relationship where a legal document concerns, defines, or affects the rights or entitlements of entities involved.
-
D.
legalForm
Indicates the specific legal structure or organizational type under which an entity is formally constituted and recognized by law.
-
E.
legalBackground
Indicates that an entity has education, training, or experience related to law or the legal profession.
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbeaa14bc81908486683decd7ae42 |
completed | March 7, 2026, 5:59 a.m. |
| PD | Predicate disambiguation | batch_69abbd9846e88190b6c2941dd9ce7749 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:44 p.m.