Triple
T33563581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warehouse Act of 1916 |
E859692
|
entity |
| Predicate | legalConceptUsed |
P59307
|
FINISHED |
| Object | negotiable warehouse receipt |
—
|
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: negotiable warehouse receipt | Statement: [Warehouse Act of 1916, legalConceptUsed, negotiable warehouse receipt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalConceptUsed Context triple: [Warehouse Act of 1916, legalConceptUsed, negotiable warehouse receipt]
-
A.
legalConcept
Indicates a relationship where something is classified or treated as a concept defined and governed by law or legal theory.
-
B.
relatedLegalConcept
Indicates that one legal concept is connected or associated with another through a relevant legal relationship or context.
-
C.
legalTermUsedFrom
Indicates that a particular legal term has been in official or recognized use starting from a specified point in time.
-
D.
legalTermIn
chosen
Indicates that a particular legal term or concept is used, defined, or applicable within a specified legal document, jurisdiction, or context.
-
E.
legalFeature
Indicates that something possesses a specific legal characteristic, status, or attribute relevant to laws or regulations.
- 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_69f3497c1d288190a844ea699914e038 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ff2636e2bc8190bba91eff91431c6e |
completed | May 9, 2026, 12:19 p.m. |
| PD | Predicate disambiguation | batch_69ff25c65be48190868480d94e1c4e89 |
completed | May 9, 2026, 12:17 p.m. |
Created at: May 1, 2026, 1:40 a.m.