Triple
T21915791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilson v. New |
E541179
|
entity |
| Predicate | involvesSubjectMatter |
P450
|
FINISHED |
| Object | railroad labor regulation |
—
|
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: railroad labor regulation | Statement: [Wilson v. New, involvesSubjectMatter, railroad labor regulation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvesSubjectMatter Context triple: [Wilson v. New, involvesSubjectMatter, railroad labor regulation]
-
A.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
C.
involves
Indicates that an entity participates in, is a part of, or is implicated within a particular event, process, or relationship.
-
D.
subjectOfLaw
Indicates that a law, legal document, or legal provision is about, concerns, or applies to the referenced subject.
-
E.
involvesIssue
Indicates that an action, event, or entity is related to, concerns, or includes a particular issue.
- 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_69e0c47c4b9c8190a5586a75f5f36453 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f123378140819090a30453c1db7f38 |
completed | April 28, 2026, 9:14 p.m. |
| PD | Predicate disambiguation | batch_69e6be9ebf4c8190892df1a8e1313f88 |
completed | April 21, 2026, 12:02 a.m. |
Created at: April 16, 2026, 7:42 p.m.