Triple
T34277430
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Schenk |
E879498
|
entity |
| Predicate | isFictionalPolicemanIn |
P31758
|
FINISHED |
| Object | Austrian television |
—
|
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: Austrian television | Statement: [Martin Schenk, isFictionalPolicemanIn, Austrian television]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFictionalPolicemanIn Context triple: [Martin Schenk, isFictionalPolicemanIn, Austrian television]
-
A.
isFictionalPersonFrom
Indicates that a fictional person originates from or is associated with a particular place or source.
-
B.
isFictionalCharacter
Indicates that the subject is a character that exists only in fiction rather than in real life.
-
C.
hasFictionalDetective
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
D.
policeCharacter
chosen
Indicates that one entity serves as a police officer or law-enforcement figure in relation to another entity.
-
E.
isFictionalAgentOf
Indicates that one entity is a fictional character or agent that acts on behalf of, or represents, another entity.
- 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_69f349b5f6648190b9420d94a4cd16e0 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f71c35327c8190884f1bfe12bd2cd7 |
completed | May 3, 2026, 9:58 a.m. |
| PD | Predicate disambiguation | batch_69f71822d0e88190ac9731c7ae5a4def |
completed | May 3, 2026, 9:40 a.m. |
Created at: May 1, 2026, 1:56 a.m.