Triple
T31355159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LVPD Homicide |
E799708
|
entity |
| Predicate | employsFictionalRole |
P25662
|
FINISHED |
| Object | homicide detective |
—
|
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: homicide detective | Statement: [LVPD Homicide, employsFictionalRole, homicide detective]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employsFictionalRole Context triple: [LVPD Homicide, employsFictionalRole, homicide detective]
-
A.
employsFictionalCharacter
Indicates that one entity (typically an organization or individual) has hired or uses the services of a fictional character in some capacity.
-
B.
hasFictionalRole
chosen
Indicates that an entity plays or is assigned a specific role within a fictional work or narrative.
-
C.
worksWithFictionalCharacter
Indicates that one entity collaborates or interacts in a work-related context with another entity that is a fictional character.
-
D.
hasFictionalPerformer
Indicates that an entity is associated with a performer who is a fictional or imaginary character rather than a real person.
-
E.
worksForCharacterPlayedBy
Indicates that one character is employed by, or works under, another character who is portrayed by a specific actor.
- 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_69f224e5e9bc8190a16339328897c4f8 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f727afd5d88190ad48735cd1b32787 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72737c42c8190a3f781a5e98868ff |
completed | May 3, 2026, 10:45 a.m. |
Created at: April 29, 2026, 9:17 p.m.