Triple
T20794704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Area Risk Protection Insurance |
E511880
|
entity |
| Predicate | insuranceLine |
P12185
|
FINISHED |
| Object | crop insurance |
—
|
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: crop insurance | Statement: [Area Risk Protection Insurance, insuranceLine, crop insurance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: insuranceLine Context triple: [Area Risk Protection Insurance, insuranceLine, crop insurance]
-
A.
insurance
Indicates a relationship where one party provides financial protection or coverage to another against specified risks or losses, typically in exchange for payment.
-
B.
insuranceType
chosen
Indicates the specific category or kind of insurance coverage associated with an entity or relationship.
-
C.
typeOfInsurer
Indicates the specific category or classification of an insurer in relation to an insurance policy or coverage.
-
D.
policyName
Indicates the specific name or title assigned to a policy associated with an entity.
-
E.
benefitProtection
Indicates that one entity provides protective advantages or safeguards that benefit another entity.
- 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.