Triple
T19684121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Phyllis Dietrichson |
E472667
|
entity |
| Predicate | plansCrimeType |
P136910
|
FINISHED |
| Object | insurance fraud |
—
|
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: insurance fraud | Statement: [Phyllis Dietrichson, plansCrimeType, insurance fraud]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: plansCrimeType Context triple: [Phyllis Dietrichson, plansCrimeType, insurance fraud]
-
A.
crimeType
Indicates the specific category or nature of the crime associated with an event or entity.
-
B.
committedCrime
Indicates that an entity has carried out or been responsible for a criminal act or offense.
-
C.
criminalType
Indicates the specific category or classification of crime associated with a criminal act or offender.
-
D.
plansRobberyIn
Indicates that an entity is preparing or organizing to commit a robbery within a specified location or jurisdiction.
-
E.
crimeLocation
Indicates that a crime occurred at, or is associated with, a particular location.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e641c225fc81909637d304891f4abd |
completed | April 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
| PDg | Predicate description generation | batch_69e532bbedf081908d801600e2af94a7 |
completed | April 19, 2026, 7:53 p.m. |
Created at: April 10, 2026, 1:45 p.m.