Triple
T24864020
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Accident Insurance Law of 1884 |
E622230
|
entity |
| Predicate | effectOnWorkers |
P159042
|
FINISHED |
| Object | legal right to accident compensation |
—
|
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: legal right to accident compensation | Statement: [Accident Insurance Law of 1884, effectOnWorkers, legal right to accident compensation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnWorkers Context triple: [Accident Insurance Law of 1884, effectOnWorkers, legal right to accident compensation]
-
A.
laborProvisionEffect
Indicates the impact or consequences that providing labor has on another entity, condition, or outcome.
-
B.
effectOnEmployers
Indicates the impact or consequences that something has on employers, such as changes to their responsibilities, costs, or working conditions.
-
C.
laborMarketEffect
Indicates the impact that an action, policy, or condition has on employment, wages, or other labor market outcomes.
-
D.
effectOnPeasants
Indicates how an action, event, or condition impacts or influences peasants.
-
E.
sectorEffect
Indicates how an action, event, or condition impacts or influences a particular sector or industry.
- F. None of above. chosen
Provenance (4 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_69e2fac350d08190b3affde1b451a8c5 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f497bc12b881908fe3386c66252bf6 |
completed | May 1, 2026, 12:08 p.m. |
| PD | Predicate disambiguation | batch_69f49366e8d08190adb4b71fe3a14683 |
completed | May 1, 2026, 11:49 a.m. |
| PDg | Predicate description generation | batch_69f497b8abb88190bb672cf6907c4b8d |
completed | May 1, 2026, 12:08 p.m. |
Created at: April 18, 2026, 5:22 a.m.