Triple
T100969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Code |
E2038
|
entity |
| Predicate | exampleCitation |
P4420
|
FINISHED |
| Object | 42 U.S.C. § 1983 |
—
|
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: 42 U.S.C. § 1983 | Statement: [United States Code, exampleCitation, 42 U.S.C. § 1983]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: exampleCitation Context triple: [United States Code, exampleCitation, 42 U.S.C. § 1983]
-
A.
citation
Indicates that one entity references, quotes, or otherwise acknowledges another entity as a source of information or authority.
-
B.
legalCitation
chosen
Indicates that one legal document, case, or authority formally references or cites another as a source of legal support or precedent.
-
C.
citationStyle
Indicates the specific formatting and referencing conventions used to cite sources in a document or publication.
-
D.
hasExample
Indicates that one entity serves as an instance, illustration, or concrete example of another entity.
-
E.
constitutionalCitation
Indicates a relationship where one entity cites, references, or is grounded in a specific provision or article of a constitution.
- 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_69a24e0a5b7c81908d52da08c60dabc4 |
completed | Feb. 28, 2026, 2:08 a.m. |
| NER | Named-entity recognition | batch_69a25760af348190bf402089c240887d |
completed | Feb. 28, 2026, 2:48 a.m. |
| PD | Predicate disambiguation | batch_69a2563921f8819087f720b1c803579f |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:12 a.m.