Triple
T30925829
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hot Girl (The Office U.S.) |
E787850
|
entity |
| Predicate | fictionalCompany |
P138782
|
FINISHED |
| Object | Dunder Mifflin |
—
|
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: Dunder Mifflin | Statement: [Hot Girl (The Office U.S.), fictionalCompany, Dunder Mifflin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fictionalCompany Context triple: [Hot Girl (The Office U.S.), fictionalCompany, Dunder Mifflin]
-
A.
fictionalCorporation
chosen
Indicates that an entity is a corporation that exists only in fiction rather than in the real world.
-
B.
fictionalOrganizationName
Indicates that the relationship specifies the name assigned to a fictional organization.
-
C.
fictionalOrganizationFeatured
Indicates that a fictional organization is prominently presented or plays a significant role within a given work or context.
-
D.
fictionalOrganizationSponsor
Indicates that one entity acts as a sponsor or patron for a fictional organization, providing support, endorsement, or resources to it.
-
E.
hasFictionalCorporation
Indicates that an entity is associated with or includes a fictional corporation within its content, setting, or narrative.
- 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_69f224bfaca88190b9d0dfcc86297fe9 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd4d1854988190be093b103a681798 |
completed | May 8, 2026, 2:40 a.m. |
| PD | Predicate disambiguation | batch_69fd4c8d1a188190897c24527337814a |
completed | May 8, 2026, 2:38 a.m. |
Created at: April 29, 2026, 8:51 p.m.