Triple
T16225623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fair Labor Division |
E393835
|
entity |
| Predicate | mayRefer |
P88346
|
FINISHED |
| Object | criminal labor law cases |
—
|
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: criminal labor law cases | Statement: [Fair Labor Division, mayRefer, criminal labor law cases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mayRefer Context triple: [Fair Labor Division, mayRefer, criminal labor law cases]
-
A.
mayReferMattersTo
chosen
Indicates that one party has the option or authority to direct or escalate certain issues, cases, or concerns to another party or body for consideration or action.
-
B.
canRefer
Indicates that one entity has the ability or permission to mention, point to, or direct attention to another entity.
-
C.
alsoRefersTo
Indicates that one term, label, or identifier is used as an alternative designation for the same entity or concept as another.
-
D.
referredTo
Indicates that one entity has mentioned, directed attention to, or cited another entity as a point of reference.
-
E.
mayEntail
Indicates that one statement or condition can logically lead to or imply the truth of another, without guaranteeing it.
- 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_69d87f204df88190a8f88923decf9835 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e23d25f8bc81909aa59b794a528db2 |
completed | April 17, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69e219e94a448190b73a4e6aa374eb4a |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:03 a.m.