Triple
T37330824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuart Gharty |
E926738
|
entity |
| Predicate | isFictionalMemberOf |
P75242
|
FINISHED |
| Object | Baltimore Police Department Homicide Unit |
—
|
NE NERFINISHED |
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: Baltimore Police Department Homicide Unit | Statement: [Stuart Gharty, isFictionalMemberOf, Baltimore Police Department Homicide Unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalMemberOf Context triple: [Stuart Gharty, isFictionalMemberOf, Baltimore Police Department Homicide Unit]
-
A.
hasFictionalMember
Indicates that a group, organization, or collection includes at least one member that is fictional rather than real.
-
B.
partOfFictionalGroup
chosen
Indicates that an entity belongs to or is a member of a specific fictional group, organization, or team.
-
C.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
D.
hasFictionalStaffMember
Indicates that an entity includes or employs a staff member who is a fictional character.
-
E.
associatedWithFictionalGroup
Indicates that an entity has a connection or affiliation with a fictional group, organization, or collective.
- 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_69f76eb386d88190a8d511aa11540dfc |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb78cbef988190b8f79d946b46e6b2 |
completed | May 6, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9ac5a08190b24ef308963fc52b |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.