Triple
T9238250
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Assistant District Attorney Ron Carver |
E221989
|
entity |
| Predicate | fictionalField |
P87724
|
FINISHED |
| Object | criminal law |
—
|
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: criminal law | Statement: [Assistant District Attorney Ron Carver, fictionalField, criminal law]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalField Context triple: [Assistant District Attorney Ron Carver, fictionalField, criminal law]
-
A.
fictionalUse
Indicates that one entity makes use of another within a fictional or imaginary context, rather than in real-world usage.
-
B.
fictionalizationOf
Indicates that one entity is a fictional or dramatized representation, adaptation, or reimagining of another (typically real or earlier) entity or event.
-
C.
fictionalOrigin
Indicates that one entity originates from, or was first introduced within, a fictional work, universe, or narrative created by another entity.
-
D.
fictionalMaterial
Indicates that something is made of, composed of, or incorporates a material that exists only in fiction or imagination.
-
E.
fictionalObject
Indicates that one entity is a fictional or imaginary object in relation to 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_69ca83ee26cc81909ac624e190597d6d |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccf09f9e908190801fe114c5e63984 |
completed | April 1, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69cc7a4765648190aa9445c4a22dc471 |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:30 p.m.