Triple
T29777959
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mark Janus |
E755432
|
entity |
| Predicate | fieldOfLegalImpact |
P113669
|
FINISHED |
| Object | labor law |
—
|
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: labor law | Statement: [Mark Janus, fieldOfLegalImpact, labor law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldOfLegalImpact Context triple: [Mark Janus, fieldOfLegalImpact, labor law]
-
A.
impactOnLaw
Indicates the effect or influence that one entity, event, or action has on laws, legal rules, or the legal system.
-
B.
appliesToFieldOfLaw
chosen
Indicates that something is relevant or applicable to a particular field or branch of law.
-
C.
notableAreaOfLaw
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
D.
legalDoctrineInfluenced
Indicates that one legal doctrine has shaped, informed, or contributed to the development or interpretation of another legal doctrine.
-
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_69f0ef878574819088c867fd1a5c8b86 |
completed | April 28, 2026, 5:33 p.m. |
| NER | Named-entity recognition | batch_69f6c1265c208190aacd2b551f8f0f82 |
completed | May 3, 2026, 3:29 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2415fc81908c23c311aebce66f |
completed | May 3, 2026, 3:12 a.m. |
Created at: April 28, 2026, 8:48 p.m.