Triple
T298980
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles deputy district attorney |
E6155
|
entity |
| Predicate | typicalCaseTypes |
P10545
|
FINISHED |
| Object | felony 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: felony cases | Statement: [Los Angeles deputy district attorney, typicalCaseTypes, felony cases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCaseTypes Context triple: [Los Angeles deputy district attorney, typicalCaseTypes, felony cases]
-
A.
typicalKey
Indicates that the referenced key is the standard or most commonly used key associated with an entity or context.
-
B.
typicalProductionType
Indicates the usual or characteristic type of production activity associated with an entity.
-
C.
typicalSegmentType
Indicates that something is classified as belonging to a usual or characteristic type of segment within a broader structure or sequence.
-
D.
standardType
Indicates that one entity is classified as the standard, canonical, or reference type for another entity or context.
-
E.
typicalVariety
Indicates that one entity is a representative or characteristic example of the variety or type defined by another entity.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea0dd1dc8190aecd5afdeb2fd74b |
completed | Feb. 28, 2026, 1:13 p.m. |
| PD | Predicate disambiguation | batch_69a2e9398df08190af40063a2de7a1d0 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea07e3bc8190bae593b3264de211 |
completed | Feb. 28, 2026, 1:13 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.