Triple
T32063303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jack Fuller |
E818804
|
entity |
| Predicate | employmentStatusAtStartOfFilm |
P86023
|
FINISHED |
| Object | recently fired |
—
|
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: recently fired | Statement: [Jack Fuller, employmentStatusAtStartOfFilm, recently fired]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: employmentStatusAtStartOfFilm Context triple: [Jack Fuller, employmentStatusAtStartOfFilm, recently fired]
-
A.
isUnemployedAtStartOf
chosen
Indicates that an individual is unemployed at the beginning of a specified time period or event.
-
B.
stateOfEmployment
Indicates that one entity’s employment status or condition is defined in relation to another entity (such as an employer, position, or employment situation).
-
C.
employerStatus
Indicates the current employment relationship or condition between an employer and a worker, such as whether the person is actively employed, terminated, retired, or on leave.
-
D.
statusAtStartOfFilm
Indicates the condition or situation an entity is in at the beginning of the film.
-
E.
legalStatusAtStartOfFilm
Indicates the legal condition or standing an entity has at the beginning of the film’s 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_69f348fdacec8190b9f74375ca3b2094 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: May 1, 2026, 12:22 a.m.