Triple
T35269116
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Los Angeles Tribune |
E1018614
|
entity |
| Predicate | fictionalStaffRole |
P61558
|
FINISHED |
| Object | city editor |
—
|
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: city editor | Statement: [Los Angeles Tribune, fictionalStaffRole, city editor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalStaffRole Context triple: [Los Angeles Tribune, fictionalStaffRole, city editor]
-
A.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
creativeRole
Indicates that an entity holds a specific creative function or responsibility in relation to another entity, such as a work or project.
-
C.
fictionalProfessionSpecialty
Indicates that a fictional character’s professional role is specialized in a particular subfield, focus area, or niche within that profession.
-
D.
fictionalUniverseRole
Indicates the role or function an entity has within a particular fictional universe or narrative setting.
-
E.
hasFictionalStaffMember
chosen
Indicates that an entity includes or employs a staff member who is a fictional character.
- 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_69f76de5c4788190896ad598ae7d6bc6 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fefb15220081908da36aac386fa582 |
completed | May 9, 2026, 9:15 a.m. |
| PD | Predicate disambiguation | batch_69fefa8e8ad48190a723fed81e9d64d0 |
completed | May 9, 2026, 9:12 a.m. |
Created at: May 3, 2026, 4:02 p.m.