Triple
T20794710
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Area Risk Protection Insurance |
E511880
|
entity |
| Predicate | policyUnit |
P61392
|
FINISHED |
| Object | insured crop in a county |
—
|
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: insured crop in a county | Statement: [Area Risk Protection Insurance, policyUnit, insured crop in a county]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyUnit Context triple: [Area Risk Protection Insurance, policyUnit, insured crop in a county]
-
A.
policyName
Indicates the specific name or title assigned to a policy associated with an entity.
-
B.
policyDetail
Indicates that there is specific descriptive or explanatory information associated with a particular policy.
-
C.
policyOutput
Indicates that a policy or decision-making process produces or yields a particular outcome, result, or output.
-
D.
policyElement
chosen
Indicates that something is a component or constituent part of a broader policy.
-
E.
policyLevel
Indicates the degree or tier of strictness, scope, or priority associated with a given policy.
- 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2abbcc8819091bb0225a0650ab6 |
completed | April 21, 2026, 12:19 a.m. |
| PD | Predicate disambiguation | batch_69e5c0575b1c81908d010223fcd1213e |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 16, 2026, 12:39 p.m.